Overview

Brought to you by YData

Dataset statistics

Number of variables	55
Number of observations	999
Missing cells	0
Missing cells (%)	0.0%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	4.3 MiB
Average record size in memory	4.4 KiB

Variable types

Text	24
Numeric	1
Categorical	30

Alerts

`Authority_Present` is highly overall correlated with `Clarity_and_Conciseness_Value` and 29 other fields	High correlation
`Clarity_and_Conciseness_Value` is highly overall correlated with `Authority_Present` and 29 other fields	High correlation
`Contains_Colon` is highly overall correlated with `Authority_Present` and 29 other fields	High correlation
`Contains_Exclamation_Mark` is highly overall correlated with `Authority_Present` and 29 other fields	High correlation
`Contains_Hyphen` is highly overall correlated with `Authority_Present` and 29 other fields	High correlation
`Contains_Numbers` is highly overall correlated with `Authority_Present` and 29 other fields	High correlation
`Contains_Question_Mark` is highly overall correlated with `Authority_Present` and 29 other fields	High correlation
`Contains_Quotes` is highly overall correlated with `Authority_Present` and 29 other fields	High correlation
`Curiosity_Present` is highly overall correlated with `Authority_Present` and 29 other fields	High correlation
`Economic_Benefit_Present` is highly overall correlated with `Authority_Present` and 29 other fields	High correlation
`Ends_With_Question_Mark` is highly overall correlated with `Authority_Present` and 29 other fields	High correlation
`Exclusivity_Present` is highly overall correlated with `Authority_Present` and 29 other fields	High correlation
`Exclusivity_Words` is highly overall correlated with `Authority_Present` and 27 other fields	High correlation
`Fear_Concern_Present` is highly overall correlated with `Authority_Present` and 29 other fields	High correlation
`Hope_Optimism_Present` is highly overall correlated with `Authority_Present` and 29 other fields	High correlation
`Indignation_Controversy_Present` is highly overall correlated with `Authority_Present` and 29 other fields	High correlation
`Length_General_Assessment` is highly overall correlated with `Authority_Present` and 29 other fields	High correlation
`Main_Category` is highly overall correlated with `Authority_Present` and 27 other fields	High correlation
`Main_Classification` is highly overall correlated with `Authority_Present` and 27 other fields	High correlation
`National_Relevance_Present` is highly overall correlated with `Authority_Present` and 29 other fields	High correlation
`Originality_and_Differentiation_Value` is highly overall correlated with `Authority_Present` and 29 other fields	High correlation
`Personal_Identification_Present` is highly overall correlated with `Authority_Present` and 29 other fields	High correlation
`Prohibition_Restriction_Present` is highly overall correlated with `Authority_Present` and 29 other fields	High correlation
`Recognized_Brand_Present` is highly overall correlated with `Authority_Present` and 29 other fields	High correlation
`Relevance_and_Timeliness_Value` is highly overall correlated with `Authority_Present` and 29 other fields	High correlation
`Solution_Present` is highly overall correlated with `Authority_Present` and 29 other fields	High correlation
`Starts_With_Number` is highly overall correlated with `Authority_Present` and 29 other fields	High correlation
`Strategic_Keyword_Usage_Value` is highly overall correlated with `Authority_Present` and 29 other fields	High correlation
`Surprise_Awe_Present` is highly overall correlated with `Authority_Present` and 29 other fields	High correlation
`Temporal_Urgency_Present` is highly overall correlated with `Authority_Present` and 29 other fields	High correlation
`Visibility` is highly overall correlated with `Authority_Present` and 29 other fields	High correlation
`Clarity_and_Conciseness_Value` is highly imbalanced (74.4%)	Imbalance
`Relevance_and_Timeliness_Value` is highly imbalanced (63.0%)	Imbalance
`Strategic_Keyword_Usage_Value` is highly imbalanced (65.7%)	Imbalance
`Contains_Question_Mark` is highly imbalanced (63.8%)	Imbalance
`Contains_Exclamation_Mark` is highly imbalanced (75.3%)	Imbalance
`Starts_With_Number` is highly imbalanced (51.9%)	Imbalance
`Ends_With_Question_Mark` is highly imbalanced (67.6%)	Imbalance
`Length_General_Assessment` is highly imbalanced (58.4%)	Imbalance
`Exclusivity_Present` is highly imbalanced (64.1%)	Imbalance
`Exclusivity_Words` is highly imbalanced (85.0%)	Imbalance
`Prohibition_Restriction_Present` is highly imbalanced (55.7%)	Imbalance
`Title` has unique values	Unique
`Visibility` has unique values	Unique

Reproduction

Analysis started	2025-07-03 06:20:42.056428
Analysis finished	2025-07-03 06:20:58.909575
Duration	16.85 seconds
Software version	ydata-profiling vv4.16.1
Download configuration	config.json

Title
Text

Unique

Distinct	999
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Memory size	180.1 KiB

Length

Max length	263
Median length	145
Mean length	83.154154
Min length	20

Characters and Unicode

Total characters	83071
Distinct characters	92
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	999 ?
Unique (%)	100.0%

Sample

1st row	Ford is forced to immediately shut down factories and halt car production as CEO admits 'day to day' struggle for brand
2nd row	New U.S. Driving License Rule for Seniors Begins July 2025 - Essential Changes for Drivers Aged 70 and Above
3rd row	Cadets who met all Air Force Academy graduation standards denied commissions because they’re transgender
4th row	The DMV confirms it - The United States is ending the benefit that allowed drivers to drive with an expired license without immediate renewal - millions of drivers will have to renew urgently
5th row	McDonald's Removing 1 Breakfast Menu Item for Good on July 2

Value	Count	Frequency (%)
to	418	3.0%
the	372	2.7%
in	274	2.0%
of	210	1.5%
a	197	1.4%
and	195	1.4%
for	194	1.4%
new	143	1.0%
with	104	0.8%
is	98	0.7%
Other values (4244)	11532	83.9%

Most occurring characters

Value	Count	Frequency (%)
	12738	15.3%
e	7675	9.2%
a	4983	6.0%
o	4803	5.8%
t	4685	5.6%
r	4540	5.5%
n	4437	5.3%
i	4398	5.3%
s	4306	5.2%
l	2860	3.4%
Other values (82)	27646	33.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	83071	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	12738	15.3%
e	7675	9.2%
a	4983	6.0%
o	4803	5.8%
t	4685	5.6%
r	4540	5.5%
n	4437	5.3%
i	4398	5.3%
s	4306	5.2%
l	2860	3.4%
Other values (82)	27646	33.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	83071	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	12738	15.3%
e	7675	9.2%
a	4983	6.0%
o	4803	5.8%
t	4685	5.6%
r	4540	5.5%
n	4437	5.3%
i	4398	5.3%
s	4306	5.2%
l	2860	3.4%
Other values (82)	27646	33.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	83071	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	12738	15.3%
e	7675	9.2%
a	4983	6.0%
o	4803	5.8%
t	4685	5.6%
r	4540	5.5%
n	4437	5.3%
i	4398	5.3%
s	4306	5.2%
l	2860	3.4%
Other values (82)	27646	33.3%

Visibility
Real number (ℝ)

High correlation Unique

Distinct	999
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	2071178.6

Minimum	582886
Maximum	22572066
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	7.9 KiB

Quantile statistics

Minimum	582886
5-th percentile	621386.7
Q1	799717
median	1215990
Q3	2304652
95-th percentile	6645731.1
Maximum	22572066
Range	21989180
Interquartile range (IQR)	1504935

Descriptive statistics

Standard deviation	2399035.7
Coefficient of variation (CV)	1.1582949
Kurtosis	19.576633
Mean	2071178.6
Median Absolute Deviation (MAD)	501087
Skewness	3.7796256
Sum	2.0691074 × 10⁹
Variance	5.7553721 × 10¹²
Monotonicity	Strictly decreasing

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
582886	1	0.1%
22572066	1	0.1%
21331409	1	0.1%
19344936	1	0.1%
18797641	1	0.1%
16353543	1	0.1%
15318643	1	0.1%
596558	1	0.1%
596772	1	0.1%
597265	1	0.1%
Other values (989)	989	99.0%

Value	Count	Frequency (%)
582886	1	0.1%
583765	1	0.1%
585267	1	0.1%
585417	1	0.1%
585428	1	0.1%
586529	1	0.1%
587557	1	0.1%
588887	1	0.1%
589230	1	0.1%
590578	1	0.1%

Value	Count	Frequency (%)
22572066	1	0.1%
21331409	1	0.1%
19344936	1	0.1%
18797641	1	0.1%
16353543	1	0.1%
15318643	1	0.1%
14627061	1	0.1%
13507301	1	0.1%
13339952	1	0.1%
13255807	1	0.1%

Cleaned_Headline
Text

Distinct	931
Distinct (%)	93.2%
Missing	0
Missing (%)	0.0%
Memory size	169.5 KiB

Length

Max length	263
Median length	142
Mean length	76.531532
Min length	0

Characters and Unicode

Total characters	76455
Distinct characters	91
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	930 ?
Unique (%)	93.1%

Sample

1st row	Ford is forced to immediately shut down factories and halt car production as CEO admits 'day to day' struggle for brand
2nd row	New U.S. Driving License Rule for Seniors Begins July 2025 - Essential Changes for Drivers Aged 70 and Above
3rd row	Cadets who met all Air Force Academy graduation standards denied commissions because they’re transgender
4th row	The DMV confirms it - The United States is ending the benefit that allowed drivers to drive with an expired license without immediate renewal - millions of drivers will have to renew urgently
5th row	McDonald's Removing 1 Breakfast Menu Item for Good on July 2

Value	Count	Frequency (%)
to	387	3.1%
the	352	2.8%
in	248	2.0%
of	193	1.5%
a	186	1.5%
for	181	1.4%
and	172	1.4%
new	134	1.1%
with	96	0.8%
is	93	0.7%
Other values (4031)	10630	83.9%

Most occurring characters

Value	Count	Frequency (%)
	11742	15.4%
e	7066	9.2%
a	4541	5.9%
o	4483	5.9%
t	4327	5.7%
r	4172	5.5%
n	4086	5.3%
i	4059	5.3%
s	3963	5.2%
l	2637	3.4%
Other values (81)	25379	33.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	76455	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	11742	15.4%
e	7066	9.2%
a	4541	5.9%
o	4483	5.9%
t	4327	5.7%
r	4172	5.5%
n	4086	5.3%
i	4059	5.3%
s	3963	5.2%
l	2637	3.4%
Other values (81)	25379	33.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	76455	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	11742	15.4%
e	7066	9.2%
a	4541	5.9%
o	4483	5.9%
t	4327	5.7%
r	4172	5.5%
n	4086	5.3%
i	4059	5.3%
s	3963	5.2%
l	2637	3.4%
Other values (81)	25379	33.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	76455	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	11742	15.4%
e	7066	9.2%
a	4541	5.9%
o	4483	5.9%
t	4327	5.7%
r	4172	5.5%
n	4086	5.3%
i	4059	5.3%
s	3963	5.2%
l	2637	3.4%
Other values (81)	25379	33.2%

Main_Category
Categorical

High correlation

Distinct	16
Distinct (%)	1.6%
Missing	0
Missing (%)	0.0%
Memory size	71.9 KiB

News_and_Current_Events	227
Finance_and_Business	168
Entertainment_and_Culture	90
Gastronomy	86
	69
Other values (11)	359

Length

Max length	26
Median length	23
Mean length	16.592593
Min length	0

Characters and Unicode

Total characters	16576
Distinct characters	35
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	0.1%

Sample

1st row	Finance_and_Business
2nd row	News_and_Current_Events
3rd row	News_and_Current_Events
4th row	News_and_Current_Events
5th row	Gastronomy

Common Values

Value	Count	Frequency (%)
News_and_Current_Events	227	22.7%
Finance_and_Business	168	16.8%
Entertainment_and_Culture	90	9.0%
Gastronomy	86	8.6%
	69	6.9%
Science	64	6.4%
Health_and_Wellness	57	5.7%
Home_and_Lifestyle	46	4.6%
Travel	45	4.5%
Curiosities_and_Miscellany	38	3.8%
Other values (6)	109	10.9%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
news_and_current_events	227	24.4%
finance_and_business	168	18.1%
entertainment_and_culture	90	9.7%
gastronomy	86	9.2%
science	64	6.9%
health_and_wellness	57	6.1%
home_and_lifestyle	46	4.9%
travel	45	4.8%
curiosities_and_miscellany	38	4.1%
sports	36	3.9%
Other values (5)	73	7.8%

Most occurring characters

Value	Count	Frequency (%)
n	2158	13.0%
e	1914	11.5%
_	1561	9.4%
s	1381	8.3%
a	1197	7.2%
t	1137	6.9%
r	839	5.1%
i	774	4.7%
u	696	4.2%
d	695	4.2%
Other values (25)	4224	25.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	16576	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
n	2158	13.0%
e	1914	11.5%
_	1561	9.4%
s	1381	8.3%
a	1197	7.2%
t	1137	6.9%
r	839	5.1%
i	774	4.7%
u	696	4.2%
d	695	4.2%
Other values (25)	4224	25.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	16576	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
n	2158	13.0%
e	1914	11.5%
_	1561	9.4%
s	1381	8.3%
a	1197	7.2%
t	1137	6.9%
r	839	5.1%
i	774	4.7%
u	696	4.2%
d	695	4.2%
Other values (25)	4224	25.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	16576	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
n	2158	13.0%
e	1914	11.5%
_	1561	9.4%
s	1381	8.3%
a	1197	7.2%
t	1137	6.9%
r	839	5.1%
i	774	4.7%
u	696	4.2%
d	695	4.2%
Other values (25)	4224	25.5%

Subcategory_1
Text

Distinct	113
Distinct (%)	11.3%
Missing	0
Missing (%)	0.0%
Memory size	70.4 KiB

Length

Max length	30
Median length	22
Mean length	15.001001
Min length	0

Characters and Unicode

Total characters	14986
Distinct characters	51
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	46 ?
Unique (%)	4.6%

Sample

1st row	Companies & Entrepreneurship
2nd row	Politics
3rd row	Politics
4th row	Politics
5th row	Restaurants & Chefs

Value	Count	Frequency (%)
	326	18.3%
companies	64	3.6%
entrepreneurship	64	3.6%
politics	53	3.0%
crime	53	3.0%
judicial	53	3.0%
recipes	53	3.0%
celebrities	51	2.9%
influencers	51	2.9%
personal	44	2.5%
Other values (143)	972	54.5%

Most occurring characters

Value	Count	Frequency (%)
e	1592	10.6%
i	1400	9.3%
n	1327	8.9%
s	1025	6.8%
r	1005	6.7%
t	934	6.2%
a	886	5.9%
	854	5.7%
o	700	4.7%
l	612	4.1%
Other values (41)	4651	31.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	14986	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	1592	10.6%
i	1400	9.3%
n	1327	8.9%
s	1025	6.8%
r	1005	6.7%
t	934	6.2%
a	886	5.9%
	854	5.7%
o	700	4.7%
l	612	4.1%
Other values (41)	4651	31.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	14986	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	1592	10.6%
i	1400	9.3%
n	1327	8.9%
s	1025	6.8%
r	1005	6.7%
t	934	6.2%
a	886	5.9%
	854	5.7%
o	700	4.7%
l	612	4.1%
Other values (41)	4651	31.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	14986	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	1592	10.6%
i	1400	9.3%
n	1327	8.9%
s	1025	6.8%
r	1005	6.7%
t	934	6.2%
a	886	5.9%
	854	5.7%
o	700	4.7%
l	612	4.1%
Other values (41)	4651	31.0%

Subcategory_2
Text

Distinct	95
Distinct (%)	9.5%
Missing	0
Missing (%)	0.0%
Memory size	60.6 KiB

Length

Max length	30
Median length	3
Mean length	4.9449449
Min length	0

Characters and Unicode

Total characters	4940
Distinct characters	51
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	71 ?
Unique (%)	7.1%

Sample

1st row	N/A
2nd row	Government
3rd row	Government
4th row	Government
5th row	N/A

Value	Count	Frequency (%)
n/a	631	61.9%
government	40	3.9%
tips	30	2.9%
advances	21	2.1%
main	21	2.1%
courses	21	2.1%
national	20	2.0%
desserts	19	1.9%
	17	1.7%
prevention	14	1.4%
Other values (114)	186	18.2%

Most occurring characters

Value	Count	Frequency (%)
A	675	13.7%
N	655	13.3%
/	633	12.8%
e	381	7.7%
s	310	6.3%
n	266	5.4%
i	242	4.9%
t	186	3.8%
r	180	3.6%
a	174	3.5%
Other values (41)	1238	25.1%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	4940	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
A	675	13.7%
N	655	13.3%
/	633	12.8%
e	381	7.7%
s	310	6.3%
n	266	5.4%
i	242	4.9%
t	186	3.8%
r	180	3.6%
a	174	3.5%
Other values (41)	1238	25.1%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	4940	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
A	675	13.7%
N	655	13.3%
/	633	12.8%
e	381	7.7%
s	310	6.3%
n	266	5.4%
i	242	4.9%
t	186	3.8%
r	180	3.6%
a	174	3.5%
Other values (41)	1238	25.1%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	4940	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
A	675	13.7%
N	655	13.3%
/	633	12.8%
e	381	7.7%
s	310	6.3%
n	266	5.4%
i	242	4.9%
t	186	3.8%
r	180	3.6%
a	174	3.5%
Other values (41)	1238	25.1%

Clarity_and_Conciseness_Value
Categorical

High correlation Imbalance

Distinct	4
Distinct (%)	0.4%
Missing	0
Missing (%)	0.0%
Memory size	59.4 KiB

High	910
	69
Medium	18
Low	2

Length

Max length	6
Median length	4
Mean length	3.7577578
Min length	0

Characters and Unicode

Total characters	3754
Distinct characters	12
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	High
2nd row	High
3rd row	High
4th row	High
5th row	High

Common Values

Value	Count	Frequency (%)
High	910	91.1%
	69	6.9%
Medium	18	1.8%
Low	2	0.2%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
high	910	97.8%
medium	18	1.9%
low	2	0.2%

Most occurring characters

Value	Count	Frequency (%)
i	928	24.7%
H	910	24.2%
g	910	24.2%
h	910	24.2%
M	18	0.5%
e	18	0.5%
d	18	0.5%
u	18	0.5%
m	18	0.5%
L	2	0.1%
Other values (2)	4	0.1%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	3754	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
i	928	24.7%
H	910	24.2%
g	910	24.2%
h	910	24.2%
M	18	0.5%
e	18	0.5%
d	18	0.5%
u	18	0.5%
m	18	0.5%
L	2	0.1%
Other values (2)	4	0.1%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	3754	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
i	928	24.7%
H	910	24.2%
g	910	24.2%
h	910	24.2%
M	18	0.5%
e	18	0.5%
d	18	0.5%
u	18	0.5%
m	18	0.5%
L	2	0.1%
Other values (2)	4	0.1%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	3754	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
i	928	24.7%
H	910	24.2%
g	910	24.2%
h	910	24.2%
M	18	0.5%
e	18	0.5%
d	18	0.5%
u	18	0.5%
m	18	0.5%
L	2	0.1%
Other values (2)	4	0.1%

Clarity_and_Conciseness_Comment
Text

Distinct	815
Distinct (%)	81.6%
Missing	0
Missing (%)	0.0%
Memory size	138.8 KiB

Length

Max length	205
Median length	144
Mean length	84.118118
Min length	0

Characters and Unicode

Total characters	84034
Distinct characters	78
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	790 ?
Unique (%)	79.1%

Sample

1st row	The main message is very clear and easy to understand, detailing Ford's production halt and the CEO's admission of struggle.
2nd row	The main message is exceptionally clear, detailing who, what, and when without ambiguity.
3rd row	The main message is very clear and direct, stating precisely what occurred and why.
4th row	The main message is very clear and direct, stating the change, its source, and its impact.
5th row	The message is clear and to the point, identifying the brand, action, and date.

Value	Count	Frequency (%)
the	1587	11.7%
and	1114	8.2%
is	826	6.1%
message	584	4.3%
clear	572	4.2%
main	554	4.1%
to	540	4.0%
easy	459	3.4%
understand	444	3.3%
headline	319	2.3%
Other values (1582)	6584	48.5%

Most occurring characters

Value	Count	Frequency (%)
	12653	15.1%
e	9351	11.1%
a	7108	8.5%
t	5669	6.7%
n	5543	6.6%
s	5535	6.6%
i	5345	6.4%
r	3669	4.4%
d	3527	4.2%
c	2932	3.5%
Other values (68)	22702	27.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	84034	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	12653	15.1%
e	9351	11.1%
a	7108	8.5%
t	5669	6.7%
n	5543	6.6%
s	5535	6.6%
i	5345	6.4%
r	3669	4.4%
d	3527	4.2%
c	2932	3.5%
Other values (68)	22702	27.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	84034	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	12653	15.1%
e	9351	11.1%
a	7108	8.5%
t	5669	6.7%
n	5543	6.6%
s	5535	6.6%
i	5345	6.4%
r	3669	4.4%
d	3527	4.2%
c	2932	3.5%
Other values (68)	22702	27.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	84034	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	12653	15.1%
e	9351	11.1%
a	7108	8.5%
t	5669	6.7%
n	5543	6.6%
s	5535	6.6%
i	5345	6.4%
r	3669	4.4%
d	3527	4.2%
c	2932	3.5%
Other values (68)	22702	27.0%

Relevance_and_Timeliness_Value
Categorical

High correlation Imbalance

Distinct	3
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	59.4 KiB

High	893
	69
Medium	37

Length

Max length	6
Median length	4
Mean length	3.7977978
Min length	0

Characters and Unicode

Total characters	3794
Distinct characters	9
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	High
2nd row	High
3rd row	High
4th row	High
5th row	High

Common Values

Value	Count	Frequency (%)
High	893	89.4%
	69	6.9%
Medium	37	3.7%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
high	893	96.0%
medium	37	4.0%

Most occurring characters

Value	Count	Frequency (%)
i	930	24.5%
H	893	23.5%
g	893	23.5%
h	893	23.5%
M	37	1.0%
e	37	1.0%
d	37	1.0%
u	37	1.0%
m	37	1.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	3794	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
i	930	24.5%
H	893	23.5%
g	893	23.5%
h	893	23.5%
M	37	1.0%
e	37	1.0%
d	37	1.0%
u	37	1.0%
m	37	1.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	3794	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
i	930	24.5%
H	893	23.5%
g	893	23.5%
h	893	23.5%
M	37	1.0%
e	37	1.0%
d	37	1.0%
u	37	1.0%
m	37	1.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	3794	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
i	930	24.5%
H	893	23.5%
g	893	23.5%
h	893	23.5%
M	37	1.0%
e	37	1.0%
d	37	1.0%
u	37	1.0%
m	37	1.0%

Relevance_and_Timeliness_Comment
Text

Distinct	931
Distinct (%)	93.2%
Missing	0
Missing (%)	0.0%
Memory size	159.1 KiB

Length

Max length	243
Median length	165
Mean length	105.42442
Min length	0

Characters and Unicode

Total characters	105319
Distinct characters	76
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	930 ?
Unique (%)	93.1%

Sample

1st row	The headline discusses a significant event for a major global brand, which is highly relevant and timely for business and general news audiences.
2nd row	Highly relevant to a specific, large demographic (seniors/drivers) and provides timely information about a future change.
3rd row	The topic of transgender rights and military policy is highly current and relevant, resonating with ongoing social and political discussions.
4th row	Highly relevant as it affects a large demographic ('millions of drivers') and requires urgent action, indicating timeliness.
5th row	Highly relevant for McDonald's customers and tied to a specific future date, July 2.

Value	Count	Frequency (%)
and	1123	7.2%
a	594	3.8%
relevant	550	3.5%
of	432	2.8%
to	430	2.8%
highly	418	2.7%
the	418	2.7%
are	368	2.4%
interest	357	2.3%
is	325	2.1%
Other values (1927)	10504	67.7%

Most occurring characters

Value	Count	Frequency (%)
	14589	13.9%
e	11710	11.1%
n	7705	7.3%
i	7639	7.3%
a	7356	7.0%
t	6855	6.5%
r	5802	5.5%
s	5466	5.2%
o	4939	4.7%
l	4614	4.4%
Other values (66)	28644	27.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	105319	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	14589	13.9%
e	11710	11.1%
n	7705	7.3%
i	7639	7.3%
a	7356	7.0%
t	6855	6.5%
r	5802	5.5%
s	5466	5.2%
o	4939	4.7%
l	4614	4.4%
Other values (66)	28644	27.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	105319	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	14589	13.9%
e	11710	11.1%
n	7705	7.3%
i	7639	7.3%
a	7356	7.0%
t	6855	6.5%
r	5802	5.5%
s	5466	5.2%
o	4939	4.7%
l	4614	4.4%
Other values (66)	28644	27.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	105319	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	14589	13.9%
e	11710	11.1%
n	7705	7.3%
i	7639	7.3%
a	7356	7.0%
t	6855	6.5%
r	5802	5.5%
s	5466	5.2%
o	4939	4.7%
l	4614	4.4%
Other values (66)	28644	27.2%

Strategic_Keyword_Usage_Value
Categorical

High correlation Imbalance

Distinct	3
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	59.4 KiB

High	902
	69
Medium	28

Length

Max length	6
Median length	4
Mean length	3.7797798
Min length	0

Characters and Unicode

Total characters	3776
Distinct characters	9
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	High
2nd row	High
3rd row	High
4th row	High
5th row	High

Common Values

Value	Count	Frequency (%)
High	902	90.3%
	69	6.9%
Medium	28	2.8%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
high	902	97.0%
medium	28	3.0%

Most occurring characters

Value	Count	Frequency (%)
i	930	24.6%
H	902	23.9%
g	902	23.9%
h	902	23.9%
M	28	0.7%
e	28	0.7%
d	28	0.7%
u	28	0.7%
m	28	0.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	3776	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
i	930	24.6%
H	902	23.9%
g	902	23.9%
h	902	23.9%
M	28	0.7%
e	28	0.7%
d	28	0.7%
u	28	0.7%
m	28	0.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	3776	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
i	930	24.6%
H	902	23.9%
g	902	23.9%
h	902	23.9%
M	28	0.7%
e	28	0.7%
d	28	0.7%
u	28	0.7%
m	28	0.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	3776	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
i	930	24.6%
H	902	23.9%
g	902	23.9%
h	902	23.9%
M	28	0.7%
e	28	0.7%
d	28	0.7%
u	28	0.7%
m	28	0.7%

Strategic_Keyword_Usage_Comment
Text

Distinct	931
Distinct (%)	93.2%
Missing	0
Missing (%)	0.0%
Memory size	179.7 KiB

Length

Max length	241
Median length	179
Mean length	119.74575
Min length	0

Characters and Unicode

Total characters	119626
Distinct characters	81
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	930 ?
Unique (%)	93.1%

Sample

1st row	Key terms like 'Ford', 'factories', 'car production', 'CEO', and 'struggle' are used effectively, making the headline highly discoverable and appealing.
2nd row	Contains highly relevant keywords like 'U.S. Driving License Rule', 'Seniors', 'July 2025', 'Essential Changes', and 'Drivers Aged 70 and Above'.
3rd row	Uses strong, specific keywords like 'Cadets', 'Air Force Academy', 'denied commissions', and 'transgender', which are highly relevant to the subject matter and discoverable.
4th row	Uses strong keywords like 'DMV', 'United States', 'expired license', 'drivers', and 'renew urgently', which are highly searchable and relevant.
5th row	Uses strong keywords like 'McDonald's', 'Breakfast Menu', and 'Removing', appealing to target audience interests.

Value	Count	Frequency (%)
and	1464	8.7%
keywords	767	4.5%
like	745	4.4%
are	699	4.1%
relevant	684	4.0%
highly	556	3.3%
to	476	2.8%
uses	443	2.6%
the	391	2.3%
audience	290	1.7%
Other values (3007)	10387	61.5%

Most occurring characters

Value	Count	Frequency (%)
	15973	13.4%
e	12652	10.6%
a	8281	6.9%
r	6815	5.7%
t	6440	5.4%
n	6323	5.3%
i	6224	5.2%
s	5965	5.0%
l	5023	4.2%
o	4758	4.0%
Other values (71)	41172	34.4%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	119626	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	15973	13.4%
e	12652	10.6%
a	8281	6.9%
r	6815	5.7%
t	6440	5.4%
n	6323	5.3%
i	6224	5.2%
s	5965	5.0%
l	5023	4.2%
o	4758	4.0%
Other values (71)	41172	34.4%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	119626	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	15973	13.4%
e	12652	10.6%
a	8281	6.9%
r	6815	5.7%
t	6440	5.4%
n	6323	5.3%
i	6224	5.2%
s	5965	5.0%
l	5023	4.2%
o	4758	4.0%
Other values (71)	41172	34.4%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	119626	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	15973	13.4%
e	12652	10.6%
a	8281	6.9%
r	6815	5.7%
t	6440	5.4%
n	6323	5.3%
i	6224	5.2%
s	5965	5.0%
l	5023	4.2%
o	4758	4.0%
Other values (71)	41172	34.4%

Originality_and_Differentiation_Value
Categorical

High correlation

Distinct	4
Distinct (%)	0.4%
Missing	0
Missing (%)	0.0%
Memory size	60.7 KiB

Medium	691
High	207
	69
Low	32

Length

Max length	6
Median length	6
Mean length	5.0750751
Min length	0

Characters and Unicode

Total characters	5070
Distinct characters	12
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Medium
2nd row	Medium
3rd row	Medium
4th row	Medium
5th row	Medium

Common Values

Value	Count	Frequency (%)
Medium	691	69.2%
High	207	20.7%
	69	6.9%
Low	32	3.2%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
medium	691	74.3%
high	207	22.3%
low	32	3.4%

Most occurring characters

Value	Count	Frequency (%)
i	898	17.7%
M	691	13.6%
e	691	13.6%
d	691	13.6%
u	691	13.6%
m	691	13.6%
H	207	4.1%
g	207	4.1%
h	207	4.1%
L	32	0.6%
Other values (2)	64	1.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	5070	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
i	898	17.7%
M	691	13.6%
e	691	13.6%
d	691	13.6%
u	691	13.6%
m	691	13.6%
H	207	4.1%
g	207	4.1%
h	207	4.1%
L	32	0.6%
Other values (2)	64	1.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	5070	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
i	898	17.7%
M	691	13.6%
e	691	13.6%
d	691	13.6%
u	691	13.6%
m	691	13.6%
H	207	4.1%
g	207	4.1%
h	207	4.1%
L	32	0.6%
Other values (2)	64	1.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	5070	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
i	898	17.7%
M	691	13.6%
e	691	13.6%
d	691	13.6%
u	691	13.6%
m	691	13.6%
H	207	4.1%
g	207	4.1%
h	207	4.1%
L	32	0.6%
Other values (2)	64	1.3%

Originality_and_Differentiation_Comment
Text

Distinct	931
Distinct (%)	93.2%
Missing	0
Missing (%)	0.0%
Memory size	181.0 KiB

Length

Max length	255
Median length	180
Mean length	121.74174
Min length	0

Characters and Unicode

Total characters	121620
Distinct characters	88
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	930 ?
Unique (%)	93.1%

Sample

1st row	While the subject matter is impactful, the phrasing is a standard news report style, not particularly unique in its linguistic approach.
2nd row	While a standard news announcement format, the specific details make it distinct, yet it lacks a unique angle or creative phrasing.
3rd row	The specific event described is notable, though the broader subject of transgender individuals in the military has been discussed before. It offers a distinct event within a known debate.
4th row	While the specific policy change is unique, the headline structure (authority confirms X - consequence) is common. Its strength lies in the immediate, widespread impact.
5th row	While common for fast-food news, the specificity of '1 Breakfast Menu Item' and 'for Good' provides some differentiation.

Value	Count	Frequency (%)
the	1557	8.3%
a	1019	5.4%
and	671	3.6%
is	665	3.5%
specific	523	2.8%
of	515	2.7%
unique	508	2.7%
common	475	2.5%
while	464	2.5%
it	322	1.7%
Other values (2504)	12096	64.3%

Most occurring characters

Value	Count	Frequency (%)
	17885	14.7%
e	11900	9.8%
i	9674	8.0%
n	7922	6.5%
t	7902	6.5%
a	7680	6.3%
o	6261	5.1%
s	5907	4.9%
r	5213	4.3%
c	4236	3.5%
Other values (78)	37040	30.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	121620	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	17885	14.7%
e	11900	9.8%
i	9674	8.0%
n	7922	6.5%
t	7902	6.5%
a	7680	6.3%
o	6261	5.1%
s	5907	4.9%
r	5213	4.3%
c	4236	3.5%
Other values (78)	37040	30.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	121620	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	17885	14.7%
e	11900	9.8%
i	9674	8.0%
n	7922	6.5%
t	7902	6.5%
a	7680	6.3%
o	6261	5.1%
s	5907	4.9%
r	5213	4.3%
c	4236	3.5%
Other values (78)	37040	30.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	121620	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	17885	14.7%
e	11900	9.8%
i	9674	8.0%
n	7922	6.5%
t	7902	6.5%
a	7680	6.3%
o	6261	5.1%
s	5907	4.9%
r	5213	4.3%
c	4236	3.5%
Other values (78)	37040	30.5%

Contains_Numbers
Categorical

High correlation

Distinct	3
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	58.0 KiB

Yes	471
No	459
	69

Length

Max length	3
Median length	2
Mean length	2.3333333
Min length	0

Characters and Unicode

Total characters	2331
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	No
2nd row	Yes
3rd row	No
4th row	Yes
5th row	Yes

Common Values

Value	Count	Frequency (%)
Yes	471	47.1%
No	459	45.9%
	69	6.9%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
yes	471	50.6%
no	459	49.4%

Most occurring characters

Value	Count	Frequency (%)
Y	471	20.2%
e	471	20.2%
s	471	20.2%
N	459	19.7%
o	459	19.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2331	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
Y	471	20.2%
e	471	20.2%
s	471	20.2%
N	459	19.7%
o	459	19.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2331	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
Y	471	20.2%
e	471	20.2%
s	471	20.2%
N	459	19.7%
o	459	19.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2331	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
Y	471	20.2%
e	471	20.2%
s	471	20.2%
N	459	19.7%
o	459	19.7%

Contains_Quotes
Categorical

High correlation

Distinct	3
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	57.7 KiB

No	800
Yes	130
	69

Length

Max length	3
Median length	2
Mean length	1.991992
Min length	0

Characters and Unicode

Total characters	1990
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Yes
2nd row	No
3rd row	No
4th row	No
5th row	No

Common Values

Value	Count	Frequency (%)
No	800	80.1%
Yes	130	13.0%
	69	6.9%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
no	800	86.0%
yes	130	14.0%

Most occurring characters

Value	Count	Frequency (%)
N	800	40.2%
o	800	40.2%
Y	130	6.5%
e	130	6.5%
s	130	6.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1990	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	800	40.2%
o	800	40.2%
Y	130	6.5%
e	130	6.5%
s	130	6.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1990	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	800	40.2%
o	800	40.2%
Y	130	6.5%
e	130	6.5%
s	130	6.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1990	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	800	40.2%
o	800	40.2%
Y	130	6.5%
e	130	6.5%
s	130	6.5%

Contains_Question_Mark
Categorical

High correlation Imbalance

Distinct	3
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	57.6 KiB

No	896
	69
Yes	34

Length

Max length	3
Median length	2
Mean length	1.8958959
Min length	0

Characters and Unicode

Total characters	1894
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	No
2nd row	No
3rd row	No
4th row	No
5th row	No

Common Values

Value	Count	Frequency (%)
No	896	89.7%
	69	6.9%
Yes	34	3.4%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
no	896	96.3%
yes	34	3.7%

Most occurring characters

Value	Count	Frequency (%)
N	896	47.3%
o	896	47.3%
Y	34	1.8%
e	34	1.8%
s	34	1.8%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1894	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	896	47.3%
o	896	47.3%
Y	34	1.8%
e	34	1.8%
s	34	1.8%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1894	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	896	47.3%
o	896	47.3%
Y	34	1.8%
e	34	1.8%
s	34	1.8%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1894	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	896	47.3%
o	896	47.3%
Y	34	1.8%
e	34	1.8%
s	34	1.8%

Contains_Colon
Categorical

High correlation

Distinct	3
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	57.7 KiB

No	808
Yes	122
	69

Length

Max length	3
Median length	2
Mean length	1.983984
Min length	0

Characters and Unicode

Total characters	1982
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	No
2nd row	No
3rd row	No
4th row	No
5th row	No

Common Values

Value	Count	Frequency (%)
No	808	80.9%
Yes	122	12.2%
	69	6.9%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
no	808	86.9%
yes	122	13.1%

Most occurring characters

Value	Count	Frequency (%)
N	808	40.8%
o	808	40.8%
Y	122	6.2%
e	122	6.2%
s	122	6.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1982	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	808	40.8%
o	808	40.8%
Y	122	6.2%
e	122	6.2%
s	122	6.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1982	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	808	40.8%
o	808	40.8%
Y	122	6.2%
e	122	6.2%
s	122	6.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1982	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	808	40.8%
o	808	40.8%
Y	122	6.2%
e	122	6.2%
s	122	6.2%

Contains_Hyphen
Categorical

High correlation

Distinct	3
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	57.8 KiB

No	684
Yes	246
	69

Length

Max length	3
Median length	2
Mean length	2.1081081
Min length	0

Characters and Unicode

Total characters	2106
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	No
2nd row	Yes
3rd row	No
4th row	Yes
5th row	No

Common Values

Value	Count	Frequency (%)
No	684	68.5%
Yes	246	24.6%
	69	6.9%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
no	684	73.5%
yes	246	26.5%

Most occurring characters

Value	Count	Frequency (%)
N	684	32.5%
o	684	32.5%
Y	246	11.7%
e	246	11.7%
s	246	11.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2106	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	684	32.5%
o	684	32.5%
Y	246	11.7%
e	246	11.7%
s	246	11.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2106	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	684	32.5%
o	684	32.5%
Y	246	11.7%
e	246	11.7%
s	246	11.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2106	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	684	32.5%
o	684	32.5%
Y	246	11.7%
e	246	11.7%
s	246	11.7%

Contains_Exclamation_Mark
Categorical

High correlation Imbalance

Distinct	3
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	57.6 KiB

No	927
	69
Yes	3

Length

Max length	3
Median length	2
Mean length	1.8648649
Min length	0

Characters and Unicode

Total characters	1863
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	No
2nd row	No
3rd row	No
4th row	No
5th row	No

Common Values

Value	Count	Frequency (%)
No	927	92.8%
	69	6.9%
Yes	3	0.3%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
no	927	99.7%
yes	3	0.3%

Most occurring characters

Value	Count	Frequency (%)
N	927	49.8%
o	927	49.8%
Y	3	0.2%
e	3	0.2%
s	3	0.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1863	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	927	49.8%
o	927	49.8%
Y	3	0.2%
e	3	0.2%
s	3	0.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1863	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	927	49.8%
o	927	49.8%
Y	3	0.2%
e	3	0.2%
s	3	0.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1863	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	927	49.8%
o	927	49.8%
Y	3	0.2%
e	3	0.2%
s	3	0.2%

Starts_With_Number
Categorical

High correlation Imbalance

Distinct	3
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	57.6 KiB

No	848
Yes	82
	69

Length

Max length	3
Median length	2
Mean length	1.9439439
Min length	0

Characters and Unicode

Total characters	1942
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	No
2nd row	No
3rd row	No
4th row	No
5th row	No

Common Values

Value	Count	Frequency (%)
No	848	84.9%
Yes	82	8.2%
	69	6.9%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
no	848	91.2%
yes	82	8.8%

Most occurring characters

Value	Count	Frequency (%)
N	848	43.7%
o	848	43.7%
Y	82	4.2%
e	82	4.2%
s	82	4.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1942	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	848	43.7%
o	848	43.7%
Y	82	4.2%
e	82	4.2%
s	82	4.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1942	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	848	43.7%
o	848	43.7%
Y	82	4.2%
e	82	4.2%
s	82	4.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1942	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	848	43.7%
o	848	43.7%
Y	82	4.2%
e	82	4.2%
s	82	4.2%

Ends_With_Question_Mark
Categorical

High correlation Imbalance

Distinct	3
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	57.6 KiB

No	908
	69
Yes	22

Length

Max length	3
Median length	2
Mean length	1.8838839
Min length	0

Characters and Unicode

Total characters	1882
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	No
2nd row	No
3rd row	No
4th row	No
5th row	No

Common Values

Value	Count	Frequency (%)
No	908	90.9%
	69	6.9%
Yes	22	2.2%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
no	908	97.6%
yes	22	2.4%

Most occurring characters

Value	Count	Frequency (%)
N	908	48.2%
o	908	48.2%
Y	22	1.2%
e	22	1.2%
s	22	1.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1882	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	908	48.2%
o	908	48.2%
Y	22	1.2%
e	22	1.2%
s	22	1.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1882	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	908	48.2%
o	908	48.2%
Y	22	1.2%
e	22	1.2%
s	22	1.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1882	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	908	48.2%
o	908	48.2%
Y	22	1.2%
e	22	1.2%
s	22	1.2%

Length_General_Assessment
Categorical

High correlation Imbalance

Distinct	3
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	64.1 KiB

Adequate	876
	69
Too long, risk of truncation	54

Length

Max length	28
Median length	8
Mean length	8.5285285
Min length	0

Characters and Unicode

Total characters	8520
Distinct characters	20
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Adequate
2nd row	Adequate
3rd row	Adequate
4th row	Adequate
5th row	Adequate

Common Values

Value	Count	Frequency (%)
Adequate	876	87.7%
	69	6.9%
Too long, risk of truncation	54	5.4%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
adequate	876	76.4%
too	54	4.7%
long	54	4.7%
risk	54	4.7%
of	54	4.7%
truncation	54	4.7%

Most occurring characters

Value	Count	Frequency (%)
e	1752	20.6%
t	984	11.5%
u	930	10.9%
a	930	10.9%
q	876	10.3%
d	876	10.3%
A	876	10.3%
o	270	3.2%
	216	2.5%
n	162	1.9%
Other values (10)	648	7.6%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	8520	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	1752	20.6%
t	984	11.5%
u	930	10.9%
a	930	10.9%
q	876	10.3%
d	876	10.3%
A	876	10.3%
o	270	3.2%
	216	2.5%
n	162	1.9%
Other values (10)	648	7.6%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	8520	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	1752	20.6%
t	984	11.5%
u	930	10.9%
a	930	10.9%
q	876	10.3%
d	876	10.3%
A	876	10.3%
o	270	3.2%
	216	2.5%
n	162	1.9%
Other values (10)	648	7.6%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	8520	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	1752	20.6%
t	984	11.5%
u	930	10.9%
a	930	10.9%
q	876	10.3%
d	876	10.3%
A	876	10.3%
o	270	3.2%
	216	2.5%
n	162	1.9%
Other values (10)	648	7.6%

Length_Number_of_Characters
Text

Distinct	127
Distinct (%)	12.7%
Missing	0
Missing (%)	0.0%
Memory size	57.7 KiB

Length

Max length	3
Median length	2
Mean length	2.007007
Min length	0

Characters and Unicode

Total characters	2005
Distinct characters	10
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	35 ?
Unique (%)	3.5%

Sample

1st row	105
2nd row	101
3rd row	106
4th row	173
5th row	57

Value	Count	Frequency (%)
59	44	4.7%
70	28	3.0%
60	27	2.9%
58	26	2.8%
69	26	2.8%
50	23	2.5%
55	23	2.5%
67	22	2.4%
104	22	2.4%
75	21	2.3%
Other values (116)	668	71.8%

Most occurring characters

Value	Count	Frequency (%)
5	316	15.8%
6	264	13.2%
1	248	12.4%
9	233	11.6%
7	205	10.2%
0	196	9.8%
4	185	9.2%
8	180	9.0%
3	95	4.7%
2	83	4.1%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2005	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
5	316	15.8%
6	264	13.2%
1	248	12.4%
9	233	11.6%
7	205	10.2%
0	196	9.8%
4	185	9.2%
8	180	9.0%
3	95	4.7%
2	83	4.1%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2005	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
5	316	15.8%
6	264	13.2%
1	248	12.4%
9	233	11.6%
7	205	10.2%
0	196	9.8%
4	185	9.2%
8	180	9.0%
3	95	4.7%
2	83	4.1%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2005	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
5	316	15.8%
6	264	13.2%
1	248	12.4%
9	233	11.6%
7	205	10.2%
0	196	9.8%
4	185	9.2%
8	180	9.0%
3	95	4.7%
2	83	4.1%

Emphatic_Capitalization_Usage
Text

Distinct	60
Distinct (%)	6.0%
Missing	0
Missing (%)	0.0%
Memory size	60.9 KiB

Length

Max length	183
Median length	2
Mean length	5.2052052
Min length	0

Characters and Unicode

Total characters	5200
Distinct characters	66
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	58 ?
Unique (%)	5.8%

Sample

1st row	No
2nd row	No
3rd row	No
4th row	No
5th row	No

Value	Count	Frequency (%)
no	873	63.8%
yes	58	4.2%
justified	29	2.1%
use	29	2.1%
impact	29	2.1%
seeks	28	2.0%
the	11	0.8%
for	11	0.8%
and	8	0.6%
a	8	0.6%
Other values (230)	285	20.8%

Most occurring characters

Value	Count	Frequency (%)
o	973	18.7%
N	884	17.0%
	439	8.4%
e	358	6.9%
s	266	5.1%
'	248	4.8%
i	211	4.1%
,	187	3.6%
t	156	3.0%
a	145	2.8%
Other values (56)	1333	25.6%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	5200	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
o	973	18.7%
N	884	17.0%
	439	8.4%
e	358	6.9%
s	266	5.1%
'	248	4.8%
i	211	4.1%
,	187	3.6%
t	156	3.0%
a	145	2.8%
Other values (56)	1333	25.6%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	5200	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
o	973	18.7%
N	884	17.0%
	439	8.4%
e	358	6.9%
s	266	5.1%
'	248	4.8%
i	211	4.1%
,	187	3.6%
t	156	3.0%
a	145	2.8%
Other values (56)	1333	25.6%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	5200	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
o	973	18.7%
N	884	17.0%
	439	8.4%
e	358	6.9%
s	266	5.1%
'	248	4.8%
i	211	4.1%
,	187	3.6%
t	156	3.0%
a	145	2.8%
Other values (56)	1333	25.6%

Main_Classification
Categorical

High correlation

Distinct	28
Distinct (%)	2.8%
Missing	0
Missing (%)	0.0%
Memory size	73.2 KiB

Declarative Simple	565
Mystery/Revelation	82
	69
Attribution ('according to', 'reveals')	46
List/Numbered	39
Other values (23)	198

Length

Max length	52
Median length	18
Mean length	17.92993
Min length	0

Characters and Unicode

Total characters	17912
Distinct characters	43
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	8 ?
Unique (%)	0.8%

Sample

1st row	Declarative Simple
2nd row	Declarative Simple
3rd row	Declarative Simple
4th row	Urgency
5th row	Declarative Simple

Common Values

Value	Count	Frequency (%)
Declarative Simple	565	56.6%
Mystery/Revelation	82	8.2%
	69	6.9%
Attribution ('according to', 'reveals')	46	4.6%
List/Numbered	39	3.9%
List/Numbered ('5 ways')	33	3.3%
Direct Question	32	3.2%
Direct Quote	29	2.9%
Superlative ('best', 'worst')	20	2.0%
Mystery/Revelation ('secret', 'truth')	14	1.4%
Other values (18)	70	7.0%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
declarative	566	30.1%
simple	565	30.1%
mystery/revelation	96	5.1%
direct	76	4.0%
list/numbered	75	4.0%
according	49	2.6%
to	49	2.6%
reveals	49	2.6%
attribution	49	2.6%
question	42	2.2%
Other values (23)	263	14.0%

Most occurring characters

Value	Count	Frequency (%)
e	2560	14.3%
i	1662	9.3%
a	1426	8.0%
t	1388	7.7%
l	1316	7.3%
r	1096	6.1%
	949	5.3%
c	773	4.3%
v	763	4.3%
m	655	3.7%
Other values (33)	5324	29.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	17912	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	2560	14.3%
i	1662	9.3%
a	1426	8.0%
t	1388	7.7%
l	1316	7.3%
r	1096	6.1%
	949	5.3%
c	773	4.3%
v	763	4.3%
m	655	3.7%
Other values (33)	5324	29.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	17912	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	2560	14.3%
i	1662	9.3%
a	1426	8.0%
t	1388	7.7%
l	1316	7.3%
r	1096	6.1%
	949	5.3%
c	773	4.3%
v	763	4.3%
m	655	3.7%
Other values (33)	5324	29.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	17912	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	2560	14.3%
i	1662	9.3%
a	1426	8.0%
t	1388	7.7%
l	1316	7.3%
r	1096	6.1%
	949	5.3%
c	773	4.3%
v	763	4.3%
m	655	3.7%
Other values (33)	5324	29.7%

Comment_Type_Justification
Text

Distinct	928
Distinct (%)	92.9%
Missing	0
Missing (%)	0.0%
Memory size	175.6 KiB

Length

Max length	333
Median length	178
Mean length	118.71171
Min length	0

Characters and Unicode

Total characters	118593
Distinct characters	84
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	925 ?
Unique (%)	92.6%

Sample

1st row	The headline presents a series of factual statements about Ford's situation and the CEO's admission without posing a question, using urgency markers, or comparing elements.
2nd row	The headline directly states a new rule and its implications, serving as a straightforward announcement.
3rd row	The headline makes a direct statement about an event, presenting it as a fact without using a question, direct quote, or emphasizing urgency.
4th row	The headline clearly states a new policy confirmed by an authority and strongly emphasizes the immediate, widespread need for action, particularly with the phrase 'renew urgently' affecting 'millions of drivers'.
5th row	The headline directly states a fact without posing a question, using a quote, or indicating urgency beyond the date.

Value	Count	Frequency (%)
a	1778	9.9%
the	1441	8.0%
headline	880	4.9%
or	572	3.2%
without	456	2.5%
question	364	2.0%
and	356	2.0%
statement	356	2.0%
about	329	1.8%
an	315	1.8%
Other values (1644)	11106	61.9%

Most occurring characters

Value	Count	Frequency (%)
	17023	14.4%
e	12052	10.2%
t	10131	8.5%
a	8796	7.4%
i	8657	7.3%
n	7590	6.4%
s	6141	5.2%
o	5778	4.9%
r	5404	4.6%
h	3889	3.3%
Other values (74)	33132	27.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	118593	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	17023	14.4%
e	12052	10.2%
t	10131	8.5%
a	8796	7.4%
i	8657	7.3%
n	7590	6.4%
s	6141	5.2%
o	5778	4.9%
r	5404	4.6%
h	3889	3.3%
Other values (74)	33132	27.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	118593	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	17023	14.4%
e	12052	10.2%
t	10131	8.5%
a	8796	7.4%
i	8657	7.3%
n	7590	6.4%
s	6141	5.2%
o	5778	4.9%
r	5404	4.6%
h	3889	3.3%
Other values (74)	33132	27.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	118593	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	17023	14.4%
e	12052	10.2%
t	10131	8.5%
a	8796	7.4%
i	8657	7.3%
n	7590	6.4%
s	6141	5.2%
o	5778	4.9%
r	5404	4.6%
h	3889	3.3%
Other values (74)	33132	27.9%

Temporal_Urgency_Present
Categorical

High correlation

Distinct	3
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	57.7 KiB

No	801
Yes	129
	69

Length

Max length	3
Median length	2
Mean length	1.990991
Min length	0

Characters and Unicode

Total characters	1989
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Yes
2nd row	Yes
3rd row	No
4th row	Yes
5th row	No

Common Values

Value	Count	Frequency (%)
No	801	80.2%
Yes	129	12.9%
	69	6.9%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
no	801	86.1%
yes	129	13.9%

Most occurring characters

Value	Count	Frequency (%)
N	801	40.3%
o	801	40.3%
Y	129	6.5%
e	129	6.5%
s	129	6.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1989	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	801	40.3%
o	801	40.3%
Y	129	6.5%
e	129	6.5%
s	129	6.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1989	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	801	40.3%
o	801	40.3%
Y	129	6.5%
e	129	6.5%
s	129	6.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1989	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	801	40.3%
o	801	40.3%
Y	129	6.5%
e	129	6.5%
s	129	6.5%

Temporal_Urgency_Words
Text

Distinct	94
Distinct (%)	9.4%
Missing	0
Missing (%)	0.0%
Memory size	59.1 KiB

Length

Max length	34
Median length	2
Mean length	3.4214214
Min length	0

Characters and Unicode

Total characters	3418
Distinct characters	59
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	73 ?
Unique (%)	7.3%

Sample

1st row	["immediately"]
2nd row	["Begins July 2025"]
3rd row	[]
4th row	["immediate","urgently"]
5th row	[]

Value	Count	Frequency (%)
	801	79.8%
this	11	1.1%
now	9	0.9%
july	8	0.8%
just	8	0.8%
tonight	6	0.6%
1	6	0.6%
week	6	0.6%
new	6	0.6%
immediately	5	0.5%
Other values (97)	138	13.7%

Most occurring characters

Value	Count	Frequency (%)
[	930	27.2%
]	930	27.2%
"	308	9.0%
e	147	4.3%
t	86	2.5%
n	83	2.4%
	74	2.2%
a	70	2.0%
i	66	1.9%
s	57	1.7%
Other values (49)	667	19.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	3418	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
[	930	27.2%
]	930	27.2%
"	308	9.0%
e	147	4.3%
t	86	2.5%
n	83	2.4%
	74	2.2%
a	70	2.0%
i	66	1.9%
s	57	1.7%
Other values (49)	667	19.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	3418	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
[	930	27.2%
]	930	27.2%
"	308	9.0%
e	147	4.3%
t	86	2.5%
n	83	2.4%
	74	2.2%
a	70	2.0%
i	66	1.9%
s	57	1.7%
Other values (49)	667	19.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	3418	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
[	930	27.2%
]	930	27.2%
"	308	9.0%
e	147	4.3%
t	86	2.5%
n	83	2.4%
	74	2.2%
a	70	2.0%
i	66	1.9%
s	57	1.7%
Other values (49)	667	19.5%

Exclusivity_Present
Categorical

High correlation Imbalance

Distinct	3
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	57.6 KiB

No	897
	69
Yes	33

Length

Max length	3
Median length	2
Mean length	1.8948949
Min length	0

Characters and Unicode

Total characters	1893
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	No
2nd row	No
3rd row	No
4th row	No
5th row	No

Common Values

Value	Count	Frequency (%)
No	897	89.8%
	69	6.9%
Yes	33	3.3%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
no	897	96.5%
yes	33	3.5%

Most occurring characters

Value	Count	Frequency (%)
N	897	47.4%
o	897	47.4%
Y	33	1.7%
e	33	1.7%
s	33	1.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1893	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	897	47.4%
o	897	47.4%
Y	33	1.7%
e	33	1.7%
s	33	1.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1893	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	897	47.4%
o	897	47.4%
Y	33	1.7%
e	33	1.7%
s	33	1.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1893	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	897	47.4%
o	897	47.4%
Y	33	1.7%
e	33	1.7%
s	33	1.7%

Exclusivity_Words
Categorical

High correlation Imbalance

Distinct	28
Distinct (%)	2.8%
Missing	0
Missing (%)	0.0%
Memory size	57.9 KiB

[]	897
	69
["Rare"]	3
["first"]	2
["only"]	2
Other values (23)	26

Length

Max length	35
Median length	2
Mean length	2.2352352
Min length	0

Characters and Unicode

Total characters	2233
Distinct characters	45
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	20 ?
Unique (%)	2.0%

Sample

1st row	[]
2nd row	[]
3rd row	[]
4th row	[]
5th row	[]

Common Values

Value	Count	Frequency (%)
[]	897	89.8%
	69	6.9%
["Rare"]	3	0.3%
["first"]	2	0.2%
["only"]	2	0.2%
["First"]	2	0.2%
["Only"]	2	0.2%
["Limited-Edition"]	2	0.2%
["Last Surviving","Rare"]	1	0.1%
["first","never before seen"]	1	0.1%
Other values (18)	18	1.8%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
	897	95.0%
first	6	0.6%
only	5	0.5%
rare	4	0.4%
limited-edition	2	0.2%
unique	2	0.2%
surviving","rare	1	0.1%
first","never	1	0.1%
before	1	0.1%
last	1	0.1%
Other values (24)	24	2.5%

Most occurring characters

Value	Count	Frequency (%)
[	930	41.6%
]	930	41.6%
"	72	3.2%
e	39	1.7%
i	29	1.3%
t	24	1.1%
r	21	0.9%
n	21	0.9%
s	16	0.7%
	14	0.6%
Other values (35)	137	6.1%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2233	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
[	930	41.6%
]	930	41.6%
"	72	3.2%
e	39	1.7%
i	29	1.3%
t	24	1.1%
r	21	0.9%
n	21	0.9%
s	16	0.7%
	14	0.6%
Other values (35)	137	6.1%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2233	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
[	930	41.6%
]	930	41.6%
"	72	3.2%
e	39	1.7%
i	29	1.3%
t	24	1.1%
r	21	0.9%
n	21	0.9%
s	16	0.7%
	14	0.6%
Other values (35)	137	6.1%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2233	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
[	930	41.6%
]	930	41.6%
"	72	3.2%
e	39	1.7%
i	29	1.3%
t	24	1.1%
r	21	0.9%
n	21	0.9%
s	16	0.7%
	14	0.6%
Other values (35)	137	6.1%

Authority_Present
Categorical

High correlation

Distinct	3
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	57.8 KiB

No	699
Yes	231
	69

Length

Max length	3
Median length	2
Mean length	2.0930931
Min length	0

Characters and Unicode

Total characters	2091
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Yes
2nd row	No
3rd row	No
4th row	Yes
5th row	No

Common Values

Value	Count	Frequency (%)
No	699	70.0%
Yes	231	23.1%
	69	6.9%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
no	699	75.2%
yes	231	24.8%

Most occurring characters

Value	Count	Frequency (%)
N	699	33.4%
o	699	33.4%
Y	231	11.0%
e	231	11.0%
s	231	11.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2091	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	699	33.4%
o	699	33.4%
Y	231	11.0%
e	231	11.0%
s	231	11.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2091	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	699	33.4%
o	699	33.4%
Y	231	11.0%
e	231	11.0%
s	231	11.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2091	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	699	33.4%
o	699	33.4%
Y	231	11.0%
e	231	11.0%
s	231	11.0%

Authority_Words
Text

Distinct	198
Distinct (%)	19.8%
Missing	0
Missing (%)	0.0%
Memory size	61.0 KiB

Length

Max length	55
Median length	2
Mean length	5.4384384
Min length	0

Characters and Unicode

Total characters	5433
Distinct characters	58
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	175 ?
Unique (%)	17.5%

Sample

1st row	["CEO admits"]
2nd row	[]
3rd row	[]
4th row	["DMV","confirms"]
5th row	[]

Value	Count	Frequency (%)
	699	67.3%
experts	9	0.9%
social	7	0.7%
tsa	6	0.6%
law	6	0.6%
scientists	6	0.6%
to	6	0.6%
security	5	0.5%
officials	5	0.5%
confirms	4	0.4%
Other values (240)	286	27.5%

Most occurring characters

Value	Count	Frequency (%)
[	930	17.1%
]	930	17.1%
"	652	12.0%
e	252	4.6%
i	203	3.7%
s	197	3.6%
o	196	3.6%
r	194	3.6%
a	176	3.2%
t	170	3.1%
Other values (48)	1533	28.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	5433	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
[	930	17.1%
]	930	17.1%
"	652	12.0%
e	252	4.6%
i	203	3.7%
s	197	3.6%
o	196	3.6%
r	194	3.6%
a	176	3.2%
t	170	3.1%
Other values (48)	1533	28.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	5433	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
[	930	17.1%
]	930	17.1%
"	652	12.0%
e	252	4.6%
i	203	3.7%
s	197	3.6%
o	196	3.6%
r	194	3.6%
a	176	3.2%
t	170	3.1%
Other values (48)	1533	28.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	5433	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
[	930	17.1%
]	930	17.1%
"	652	12.0%
e	252	4.6%
i	203	3.7%
s	197	3.6%
o	196	3.6%
r	194	3.6%
a	176	3.2%
t	170	3.1%
Other values (48)	1533	28.2%

Solution_Present
Categorical

High correlation

Distinct	3
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	57.7 KiB

No	773
Yes	157
	69

Length

Max length	3
Median length	2
Mean length	2.019019
Min length	0

Characters and Unicode

Total characters	2017
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	No
2nd row	No
3rd row	No
4th row	No
5th row	No

Common Values

Value	Count	Frequency (%)
No	773	77.4%
Yes	157	15.7%
	69	6.9%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
no	773	83.1%
yes	157	16.9%

Most occurring characters

Value	Count	Frequency (%)
N	773	38.3%
o	773	38.3%
Y	157	7.8%
e	157	7.8%
s	157	7.8%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2017	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	773	38.3%
o	773	38.3%
Y	157	7.8%
e	157	7.8%
s	157	7.8%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2017	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	773	38.3%
o	773	38.3%
Y	157	7.8%
e	157	7.8%
s	157	7.8%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2017	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	773	38.3%
o	773	38.3%
Y	157	7.8%
e	157	7.8%
s	157	7.8%

Solution_Words
Text

Distinct	143
Distinct (%)	14.3%
Missing	0
Missing (%)	0.0%
Memory size	59.7 KiB

Length

Max length	44
Median length	2
Mean length	4.1061061
Min length	0

Characters and Unicode

Total characters	4102
Distinct characters	56
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	130 ?
Unique (%)	13.0%

Sample

1st row	[]
2nd row	[]
3rd row	[]
4th row	[]
5th row	[]

Value	Count	Frequency (%)
	774	73.7%
to	23	2.2%
how	18	1.7%
best	8	0.8%
way	5	0.5%
watch	4	0.4%
clean	4	0.4%
easy	4	0.4%
restored	3	0.3%
it	3	0.3%
Other values (180)	204	19.4%

Most occurring characters

Value	Count	Frequency (%)
[	930	22.7%
]	930	22.7%
"	412	10.0%
e	231	5.6%
o	138	3.4%
t	136	3.3%
	120	2.9%
i	93	2.3%
r	89	2.2%
a	86	2.1%
Other values (46)	937	22.8%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	4102	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
[	930	22.7%
]	930	22.7%
"	412	10.0%
e	231	5.6%
o	138	3.4%
t	136	3.3%
	120	2.9%
i	93	2.3%
r	89	2.2%
a	86	2.1%
Other values (46)	937	22.8%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	4102	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
[	930	22.7%
]	930	22.7%
"	412	10.0%
e	231	5.6%
o	138	3.4%
t	136	3.3%
	120	2.9%
i	93	2.3%
r	89	2.2%
a	86	2.1%
Other values (46)	937	22.8%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	4102	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
[	930	22.7%
]	930	22.7%
"	412	10.0%
e	231	5.6%
o	138	3.4%
t	136	3.3%
	120	2.9%
i	93	2.3%
r	89	2.2%
a	86	2.1%
Other values (46)	937	22.8%

Economic_Benefit_Present
Categorical

High correlation

Distinct	3
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	57.7 KiB

No	790
Yes	140
	69

Length

Max length	3
Median length	2
Mean length	2.002002
Min length	0

Characters and Unicode

Total characters	2000
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	No
2nd row	No
3rd row	No
4th row	No
5th row	No

Common Values

Value	Count	Frequency (%)
No	790	79.1%
Yes	140	14.0%
	69	6.9%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
no	790	84.9%
yes	140	15.1%

Most occurring characters

Value	Count	Frequency (%)
N	790	39.5%
o	790	39.5%
Y	140	7.0%
e	140	7.0%
s	140	7.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2000	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	790	39.5%
o	790	39.5%
Y	140	7.0%
e	140	7.0%
s	140	7.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2000	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	790	39.5%
o	790	39.5%
Y	140	7.0%
e	140	7.0%
s	140	7.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2000	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	790	39.5%
o	790	39.5%
Y	140	7.0%
e	140	7.0%
s	140	7.0%

Economic_Benefit_Words
Text

Distinct	134
Distinct (%)	13.4%
Missing	0
Missing (%)	0.0%
Memory size	60.3 KiB

Length

Max length	59
Median length	2
Mean length	4.4494494
Min length	0

Characters and Unicode

Total characters	4445
Distinct characters	68
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	124 ?
Unique (%)	12.4%

Sample

1st row	[]
2nd row	[]
3rd row	[]
4th row	[]
5th row	[]

Value	Count	Frequency (%)
	790	76.6%
million	16	1.6%
refunds	4	0.4%
benefits	4	0.4%
free	3	0.3%
valuable	3	0.3%
billion	3	0.3%
fines	3	0.3%
tax	3	0.3%
affordable	2	0.2%
Other values (182)	200	19.4%

Most occurring characters

Value	Count	Frequency (%)
[	930	20.9%
]	930	20.9%
"	464	10.4%
e	174	3.9%
l	147	3.3%
i	147	3.3%
n	132	3.0%
,	113	2.5%
o	112	2.5%
	101	2.3%
Other values (58)	1195	26.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	4445	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
[	930	20.9%
]	930	20.9%
"	464	10.4%
e	174	3.9%
l	147	3.3%
i	147	3.3%
n	132	3.0%
,	113	2.5%
o	112	2.5%
	101	2.3%
Other values (58)	1195	26.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	4445	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
[	930	20.9%
]	930	20.9%
"	464	10.4%
e	174	3.9%
l	147	3.3%
i	147	3.3%
n	132	3.0%
,	113	2.5%
o	112	2.5%
	101	2.3%
Other values (58)	1195	26.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	4445	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
[	930	20.9%
]	930	20.9%
"	464	10.4%
e	174	3.9%
l	147	3.3%
i	147	3.3%
n	132	3.0%
,	113	2.5%
o	112	2.5%
	101	2.3%
Other values (58)	1195	26.9%

Prohibition_Restriction_Present
Categorical

High correlation Imbalance

Distinct	3
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	57.6 KiB

No	865
	69
Yes	65

Length

Max length	3
Median length	2
Mean length	1.9269269
Min length	0

Characters and Unicode

Total characters	1925
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	No
2nd row	No
3rd row	Yes
4th row	Yes
5th row	No

Common Values

Value	Count	Frequency (%)
No	865	86.6%
	69	6.9%
Yes	65	6.5%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
no	865	93.0%
yes	65	7.0%

Most occurring characters

Value	Count	Frequency (%)
N	865	44.9%
o	865	44.9%
Y	65	3.4%
e	65	3.4%
s	65	3.4%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1925	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	865	44.9%
o	865	44.9%
Y	65	3.4%
e	65	3.4%
s	65	3.4%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1925	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	865	44.9%
o	865	44.9%
Y	65	3.4%
e	65	3.4%
s	65	3.4%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1925	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	865	44.9%
o	865	44.9%
Y	65	3.4%
e	65	3.4%
s	65	3.4%

Prohibition_Restriction_Words
Text

Distinct	58
Distinct (%)	5.8%
Missing	0
Missing (%)	0.0%
Memory size	58.7 KiB

Length

Max length	68
Median length	2
Mean length	2.987988
Min length	0

Characters and Unicode

Total characters	2985
Distinct characters	50
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	49 ?
Unique (%)	4.9%

Sample

1st row	[]
2nd row	[]
3rd row	["denied"]
4th row	["ending the benefit","without immediate renewal"]
5th row	[]

Value	Count	Frequency (%)
	865	86.2%
to	8	0.8%
not	5	0.5%
avoid	4	0.4%
away	3	0.3%
banned	3	0.3%
ban	3	0.3%
banning	2	0.2%
neither	2	0.2%
no	2	0.2%
Other values (98)	106	10.6%

Most occurring characters

Value	Count	Frequency (%)
[	930	31.2%
]	930	31.2%
"	174	5.8%
e	98	3.3%
o	74	2.5%
	73	2.4%
n	72	2.4%
i	69	2.3%
t	63	2.1%
r	50	1.7%
Other values (40)	452	15.1%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2985	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
[	930	31.2%
]	930	31.2%
"	174	5.8%
e	98	3.3%
o	74	2.5%
	73	2.4%
n	72	2.4%
i	69	2.3%
t	63	2.1%
r	50	1.7%
Other values (40)	452	15.1%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2985	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
[	930	31.2%
]	930	31.2%
"	174	5.8%
e	98	3.3%
o	74	2.5%
	73	2.4%
n	72	2.4%
i	69	2.3%
t	63	2.1%
r	50	1.7%
Other values (40)	452	15.1%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2985	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
[	930	31.2%
]	930	31.2%
"	174	5.8%
e	98	3.3%
o	74	2.5%
	73	2.4%
n	72	2.4%
i	69	2.3%
t	63	2.1%
r	50	1.7%
Other values (40)	452	15.1%

National_Relevance_Present
Categorical

High correlation

Distinct	3
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	57.9 KiB

No	562
Yes	368
	69

Length

Max length	3
Median length	2
Mean length	2.2302302
Min length	0

Characters and Unicode

Total characters	2228
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	No
2nd row	Yes
3rd row	No
4th row	Yes
5th row	No

Common Values

Value	Count	Frequency (%)
No	562	56.3%
Yes	368	36.8%
	69	6.9%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
no	562	60.4%
yes	368	39.6%

Most occurring characters

Value	Count	Frequency (%)
N	562	25.2%
o	562	25.2%
Y	368	16.5%
e	368	16.5%
s	368	16.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2228	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	562	25.2%
o	562	25.2%
Y	368	16.5%
e	368	16.5%
s	368	16.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2228	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	562	25.2%
o	562	25.2%
Y	368	16.5%
e	368	16.5%
s	368	16.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2228	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	562	25.2%
o	562	25.2%
Y	368	16.5%
e	368	16.5%
s	368	16.5%

National_Relevance_Words
Text

Distinct	222
Distinct (%)	22.2%
Missing	0
Missing (%)	0.0%
Memory size	63.4 KiB

Length

Max length	123
Median length	2
Mean length	7.8978979
Min length	0

Characters and Unicode

Total characters	7890
Distinct characters	59
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	185 ?
Unique (%)	18.5%

Sample

1st row	[]
2nd row	["U.S."]
3rd row	[]
4th row	["United States"]
5th row	[]

Value	Count	Frequency (%)
	562	50.4%
new	35	3.1%
york	33	3.0%
california	26	2.3%
states	21	1.9%
texas	15	1.3%
state	15	1.3%
us	14	1.3%
illinois	11	1.0%
u.s	10	0.9%
Other values (280)	373	33.5%

Most occurring characters

Value	Count	Frequency (%)
"	1170	14.8%
[	930	11.8%
]	930	11.8%
a	460	5.8%
i	361	4.6%
e	352	4.5%
n	306	3.9%
o	295	3.7%
t	281	3.6%
r	267	3.4%
Other values (49)	2538	32.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	7890	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
"	1170	14.8%
[	930	11.8%
]	930	11.8%
a	460	5.8%
i	361	4.6%
e	352	4.5%
n	306	3.9%
o	295	3.7%
t	281	3.6%
r	267	3.4%
Other values (49)	2538	32.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	7890	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
"	1170	14.8%
[	930	11.8%
]	930	11.8%
a	460	5.8%
i	361	4.6%
e	352	4.5%
n	306	3.9%
o	295	3.7%
t	281	3.6%
r	267	3.4%
Other values (49)	2538	32.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	7890	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
"	1170	14.8%
[	930	11.8%
]	930	11.8%
a	460	5.8%
i	361	4.6%
e	352	4.5%
n	306	3.9%
o	295	3.7%
t	281	3.6%
r	267	3.4%
Other values (49)	2538	32.2%

Recognized_Brand_Present
Categorical

High correlation

Distinct	3
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	57.9 KiB

No	595
Yes	335
	69

Length

Max length	3
Median length	2
Mean length	2.1971972
Min length	0

Characters and Unicode

Total characters	2195
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Yes
2nd row	No
3rd row	No
4th row	Yes
5th row	Yes

Common Values

Value	Count	Frequency (%)
No	595	59.6%
Yes	335	33.5%
	69	6.9%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
no	595	64.0%
yes	335	36.0%

Most occurring characters

Value	Count	Frequency (%)
N	595	27.1%
o	595	27.1%
Y	335	15.3%
e	335	15.3%
s	335	15.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2195	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	595	27.1%
o	595	27.1%
Y	335	15.3%
e	335	15.3%
s	335	15.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2195	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	595	27.1%
o	595	27.1%
Y	335	15.3%
e	335	15.3%
s	335	15.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2195	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	595	27.1%
o	595	27.1%
Y	335	15.3%
e	335	15.3%
s	335	15.3%

Recognized_Brand_Words
Text

Distinct	262
Distinct (%)	26.2%
Missing	0
Missing (%)	0.0%
Memory size	64.1 KiB

Length

Max length	154
Median length	2
Mean length	8.0750751
Min length	0

Characters and Unicode

Total characters	8067
Distinct characters	72
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	223 ?
Unique (%)	22.3%

Sample

1st row	["Ford"]
2nd row	[]
3rd row	[]
4th row	["DMV"]
5th row	["McDonald's"]

Value	Count	Frequency (%)
	597	48.5%
security	14	1.1%
social	13	1.1%
walmart	11	0.9%
air	8	0.6%
tsa	7	0.6%
elon	5	0.4%
dollar	5	0.4%
webb	5	0.4%
mcdonald's	5	0.4%
Other values (442)	561	45.6%

Most occurring characters

Value	Count	Frequency (%)
"	1024	12.7%
[	930	11.5%
]	930	11.5%
e	446	5.5%
a	392	4.9%
r	344	4.3%
	301	3.7%
i	292	3.6%
o	283	3.5%
l	248	3.1%
Other values (62)	2877	35.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	8067	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
"	1024	12.7%
[	930	11.5%
]	930	11.5%
e	446	5.5%
a	392	4.9%
r	344	4.3%
	301	3.7%
i	292	3.6%
o	283	3.5%
l	248	3.1%
Other values (62)	2877	35.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	8067	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
"	1024	12.7%
[	930	11.5%
]	930	11.5%
e	446	5.5%
a	392	4.9%
r	344	4.3%
	301	3.7%
i	292	3.6%
o	283	3.5%
l	248	3.1%
Other values (62)	2877	35.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	8067	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
"	1024	12.7%
[	930	11.5%
]	930	11.5%
e	446	5.5%
a	392	4.9%
r	344	4.3%
	301	3.7%
i	292	3.6%
o	283	3.5%
l	248	3.1%
Other values (62)	2877	35.7%

Curiosity_Present
Categorical

High correlation

Distinct	3
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	58.3 KiB

Yes	725
No	205
	69

Length

Max length	3
Median length	3
Mean length	2.5875876
Min length	0

Characters and Unicode

Total characters	2585
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	No
2nd row	Yes
3rd row	No
4th row	No
5th row	Yes

Common Values

Value	Count	Frequency (%)
Yes	725	72.6%
No	205	20.5%
	69	6.9%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
yes	725	78.0%
no	205	22.0%

Most occurring characters

Value	Count	Frequency (%)
Y	725	28.0%
e	725	28.0%
s	725	28.0%
N	205	7.9%
o	205	7.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2585	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
Y	725	28.0%
e	725	28.0%
s	725	28.0%
N	205	7.9%
o	205	7.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2585	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
Y	725	28.0%
e	725	28.0%
s	725	28.0%
N	205	7.9%
o	205	7.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2585	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
Y	725	28.0%
e	725	28.0%
s	725	28.0%
N	205	7.9%
o	205	7.9%

Curiosity_Evidence
Text

Distinct	727
Distinct (%)	72.8%
Missing	0
Missing (%)	0.0%
Memory size	170.1 KiB

Length

Max length	273
Median length	207
Mean length	101.34034
Min length	0

Characters and Unicode

Total characters	101239
Distinct characters	86
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	726 ?
Unique (%)	72.7%

Sample

1st row
2nd row	The phrase 'Essential Changes' prompts readers to seek information on what those changes entail.
3rd row
4th row
5th row	The headline creates an information gap by not revealing which specific breakfast item is being removed.

Value	Count	Frequency (%)
the	1676	10.4%
and	557	3.4%
to	487	3.0%
an	460	2.8%
gap	457	2.8%
information	451	2.8%
creates	446	2.8%
about	377	2.3%
reader	319	2.0%
a	295	1.8%
Other values (2496)	10623	65.8%

Most occurring characters

Value	Count	Frequency (%)
	15422	15.2%
e	9960	9.8%
a	7347	7.3%
t	6884	6.8%
i	6258	6.2%
n	5866	5.8%
r	5693	5.6%
o	5654	5.6%
s	5034	5.0%
h	3992	3.9%
Other values (76)	29129	28.8%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	101239	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	15422	15.2%
e	9960	9.8%
a	7347	7.3%
t	6884	6.8%
i	6258	6.2%
n	5866	5.8%
r	5693	5.6%
o	5654	5.6%
s	5034	5.0%
h	3992	3.9%
Other values (76)	29129	28.8%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	101239	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	15422	15.2%
e	9960	9.8%
a	7347	7.3%
t	6884	6.8%
i	6258	6.2%
n	5866	5.8%
r	5693	5.6%
o	5654	5.6%
s	5034	5.0%
h	3992	3.9%
Other values (76)	29129	28.8%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	101239	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	15422	15.2%
e	9960	9.8%
a	7347	7.3%
t	6884	6.8%
i	6258	6.2%
n	5866	5.8%
r	5693	5.6%
o	5654	5.6%
s	5034	5.0%
h	3992	3.9%
Other values (76)	29129	28.8%

Fear_Concern_Present
Categorical

High correlation

Distinct	3
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	57.9 KiB

No	566
Yes	364
	69

Length

Max length	3
Median length	2
Mean length	2.2262262
Min length	0

Characters and Unicode

Total characters	2224
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Yes
2nd row	No
3rd row	No
4th row	Yes
5th row	Yes

Common Values

Value	Count	Frequency (%)
No	566	56.7%
Yes	364	36.4%
	69	6.9%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
no	566	60.9%
yes	364	39.1%

Most occurring characters

Value	Count	Frequency (%)
N	566	25.4%
o	566	25.4%
Y	364	16.4%
e	364	16.4%
s	364	16.4%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2224	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	566	25.4%
o	566	25.4%
Y	364	16.4%
e	364	16.4%
s	364	16.4%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2224	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	566	25.4%
o	566	25.4%
Y	364	16.4%
e	364	16.4%
s	364	16.4%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2224	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	566	25.4%
o	566	25.4%
Y	364	16.4%
e	364	16.4%
s	364	16.4%

Fear_Concern_Evidence
Text

Distinct	366
Distinct (%)	36.6%
Missing	0
Missing (%)	0.0%
Memory size	105.4 KiB

Length

Max length	240
Median length	0
Mean length	46.847848
Min length	0

Characters and Unicode

Total characters	46801
Distinct characters	78
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	365 ?
Unique (%)	36.5%

Sample

1st row	The phrases 'forced to immediately shut down factories', 'halt car production', and 'struggle for brand' evoke concern about economic stability and the future of a major company.
2nd row
3rd row
4th row	The phrase 'millions of drivers will have to renew urgently' directly implies potential negative consequences or significant inconvenience if the reader fails to act, evoking concern.
5th row	The phrase 'Removing... for Good' could evoke concern among customers about a favorite item.

Value	Count	Frequency (%)
the	481	6.9%
and	332	4.7%
concern	304	4.3%
of	244	3.5%
a	199	2.8%
or	167	2.4%
about	149	2.1%
for	148	2.1%
evoke	124	1.8%
potential	122	1.7%
Other values (1577)	4720	67.5%

Most occurring characters

Value	Count	Frequency (%)
	6625	14.2%
e	4808	10.3%
n	3338	7.1%
o	3042	6.5%
a	2973	6.4%
i	2758	5.9%
t	2725	5.8%
r	2671	5.7%
s	2393	5.1%
c	1988	4.2%
Other values (68)	13480	28.8%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	46801	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	6625	14.2%
e	4808	10.3%
n	3338	7.1%
o	3042	6.5%
a	2973	6.4%
i	2758	5.9%
t	2725	5.8%
r	2671	5.7%
s	2393	5.1%
c	1988	4.2%
Other values (68)	13480	28.8%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	46801	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	6625	14.2%
e	4808	10.3%
n	3338	7.1%
o	3042	6.5%
a	2973	6.4%
i	2758	5.9%
t	2725	5.8%
r	2671	5.7%
s	2393	5.1%
c	1988	4.2%
Other values (68)	13480	28.8%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	46801	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	6625	14.2%
e	4808	10.3%
n	3338	7.1%
o	3042	6.5%
a	2973	6.4%
i	2758	5.9%
t	2725	5.8%
r	2671	5.7%
s	2393	5.1%
c	1988	4.2%
Other values (68)	13480	28.8%

Surprise_Awe_Present
Categorical

High correlation

Distinct	3
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	57.9 KiB

No	615
Yes	315
	69

Length

Max length	3
Median length	2
Mean length	2.1771772
Min length	0

Characters and Unicode

Total characters	2175
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	No
2nd row	No
3rd row	No
4th row	No
5th row	No

Common Values

Value	Count	Frequency (%)
No	615	61.6%
Yes	315	31.5%
	69	6.9%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
no	615	66.1%
yes	315	33.9%

Most occurring characters

Value	Count	Frequency (%)
N	615	28.3%
o	615	28.3%
Y	315	14.5%
e	315	14.5%
s	315	14.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2175	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	615	28.3%
o	615	28.3%
Y	315	14.5%
e	315	14.5%
s	315	14.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2175	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	615	28.3%
o	615	28.3%
Y	315	14.5%
e	315	14.5%
s	315	14.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2175	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	615	28.3%
o	615	28.3%
Y	315	14.5%
e	315	14.5%
s	315	14.5%

Surprise_Awe_Evidence
Text

Distinct	317
Distinct (%)	31.7%
Missing	0
Missing (%)	0.0%
Memory size	95.1 KiB

Length

Max length	206
Median length	0
Mean length	36.406406
Min length	0

Characters and Unicode

Total characters	36370
Distinct characters	79
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	316 ?
Unique (%)	31.6%

Sample

1st row
2nd row
3rd row
4th row
5th row

Value	Count	Frequency (%)
the	500	8.6%
of	333	5.7%
and	288	4.9%
a	279	4.8%
surprise	159	2.7%
unexpected	102	1.8%
is	97	1.7%
can	96	1.6%
evoke	91	1.6%
surprising	90	1.5%
Other values (1552)	3791	65.1%

Most occurring characters

Value	Count	Frequency (%)
	5510	15.1%
e	3954	10.9%
n	2317	6.4%
a	2285	6.3%
i	2239	6.2%
s	2165	6.0%
t	1962	5.4%
r	1904	5.2%
o	1825	5.0%
d	1032	2.8%
Other values (69)	11177	30.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	36370	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	5510	15.1%
e	3954	10.9%
n	2317	6.4%
a	2285	6.3%
i	2239	6.2%
s	2165	6.0%
t	1962	5.4%
r	1904	5.2%
o	1825	5.0%
d	1032	2.8%
Other values (69)	11177	30.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	36370	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	5510	15.1%
e	3954	10.9%
n	2317	6.4%
a	2285	6.3%
i	2239	6.2%
s	2165	6.0%
t	1962	5.4%
r	1904	5.2%
o	1825	5.0%
d	1032	2.8%
Other values (69)	11177	30.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	36370	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	5510	15.1%
e	3954	10.9%
n	2317	6.4%
a	2285	6.3%
i	2239	6.2%
s	2165	6.0%
t	1962	5.4%
r	1904	5.2%
o	1825	5.0%
d	1032	2.8%
Other values (69)	11177	30.7%

Indignation_Controversy_Present
Categorical

High correlation

Distinct	3
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	57.7 KiB

No	769
Yes	161
	69

Length

Max length	3
Median length	2
Mean length	2.023023
Min length	0

Characters and Unicode

Total characters	2021
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	No
2nd row	No
3rd row	Yes
4th row	No
5th row	No

Common Values

Value	Count	Frequency (%)
No	769	77.0%
Yes	161	16.1%
	69	6.9%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
no	769	82.7%
yes	161	17.3%

Most occurring characters

Value	Count	Frequency (%)
N	769	38.1%
o	769	38.1%
Y	161	8.0%
e	161	8.0%
s	161	8.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2021	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	769	38.1%
o	769	38.1%
Y	161	8.0%
e	161	8.0%
s	161	8.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2021	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	769	38.1%
o	769	38.1%
Y	161	8.0%
e	161	8.0%
s	161	8.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2021	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	769	38.1%
o	769	38.1%
Y	161	8.0%
e	161	8.0%
s	161	8.0%

Indignation_Controversy_Evidence
Text

Distinct	163
Distinct (%)	16.3%
Missing	0
Missing (%)	0.0%
Memory size	81.4 KiB

Length

Max length	264
Median length	0
Mean length	22.155155
Min length	0

Characters and Unicode

Total characters	22133
Distinct characters	75
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	162 ?
Unique (%)	16.2%

Sample

1st row
2nd row
3rd row	The phrase 'denied commissions because they’re transgender' directly implies a potential act of discrimination, which is highly likely to provoke indignation and controversy among various audiences.
4th row
5th row

Value	Count	Frequency (%)
the	199	6.1%
and	156	4.8%
a	130	4.0%
indignation	111	3.4%
of	110	3.4%
or	88	2.7%
to	80	2.4%
provoke	76	2.3%
debate	71	2.2%
can	66	2.0%
Other values (1004)	2179	66.7%

Most occurring characters

Value	Count	Frequency (%)
	3104	14.0%
e	1990	9.0%
n	1724	7.8%
i	1630	7.4%
a	1553	7.0%
o	1552	7.0%
t	1416	6.4%
r	1179	5.3%
s	1018	4.6%
d	776	3.5%
Other values (65)	6191	28.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	22133	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	3104	14.0%
e	1990	9.0%
n	1724	7.8%
i	1630	7.4%
a	1553	7.0%
o	1552	7.0%
t	1416	6.4%
r	1179	5.3%
s	1018	4.6%
d	776	3.5%
Other values (65)	6191	28.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	22133	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	3104	14.0%
e	1990	9.0%
n	1724	7.8%
i	1630	7.4%
a	1553	7.0%
o	1552	7.0%
t	1416	6.4%
r	1179	5.3%
s	1018	4.6%
d	776	3.5%
Other values (65)	6191	28.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	22133	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	3104	14.0%
e	1990	9.0%
n	1724	7.8%
i	1630	7.4%
a	1553	7.0%
o	1552	7.0%
t	1416	6.4%
r	1179	5.3%
s	1018	4.6%
d	776	3.5%
Other values (65)	6191	28.0%

Hope_Optimism_Present
Categorical

High correlation

Distinct	3
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	57.9 KiB

No	620
Yes	310
	69

Length

Max length	3
Median length	2
Mean length	2.1721722
Min length	0

Characters and Unicode

Total characters	2170
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	No
2nd row	No
3rd row	No
4th row	No
5th row	No

Common Values

Value	Count	Frequency (%)
No	620	62.1%
Yes	310	31.0%
	69	6.9%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
no	620	66.7%
yes	310	33.3%

Most occurring characters

Value	Count	Frequency (%)
N	620	28.6%
o	620	28.6%
Y	310	14.3%
e	310	14.3%
s	310	14.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2170	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	620	28.6%
o	620	28.6%
Y	310	14.3%
e	310	14.3%
s	310	14.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2170	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	620	28.6%
o	620	28.6%
Y	310	14.3%
e	310	14.3%
s	310	14.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2170	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	620	28.6%
o	620	28.6%
Y	310	14.3%
e	310	14.3%
s	310	14.3%

Hope_Optimism_Evidence
Text

Distinct	312
Distinct (%)	31.2%
Missing	0
Missing (%)	0.0%
Memory size	92.3 KiB

Length

Max length	206
Median length	0
Mean length	35.454454
Min length	0

Characters and Unicode

Total characters	35419
Distinct characters	75
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	311 ?
Unique (%)	31.1%

Sample

1st row
2nd row
3rd row
4th row
5th row

Value	Count	Frequency (%)
a	430	7.8%
the	330	6.0%
and	279	5.1%
of	218	4.0%
for	206	3.7%
positive	175	3.2%
hope	160	2.9%
to	102	1.9%
offers	95	1.7%
sense	73	1.3%
Other values (1243)	3427	62.4%

Most occurring characters

Value	Count	Frequency (%)
	5184	14.6%
e	3841	10.8%
o	2616	7.4%
i	2333	6.6%
a	2064	5.8%
s	2042	5.8%
n	1948	5.5%
t	1920	5.4%
r	1642	4.6%
l	1081	3.1%
Other values (65)	10748	30.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	35419	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	5184	14.6%
e	3841	10.8%
o	2616	7.4%
i	2333	6.6%
a	2064	5.8%
s	2042	5.8%
n	1948	5.5%
t	1920	5.4%
r	1642	4.6%
l	1081	3.1%
Other values (65)	10748	30.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	35419	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	5184	14.6%
e	3841	10.8%
o	2616	7.4%
i	2333	6.6%
a	2064	5.8%
s	2042	5.8%
n	1948	5.5%
t	1920	5.4%
r	1642	4.6%
l	1081	3.1%
Other values (65)	10748	30.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	35419	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	5184	14.6%
e	3841	10.8%
o	2616	7.4%
i	2333	6.6%
a	2064	5.8%
s	2042	5.8%
n	1948	5.5%
t	1920	5.4%
r	1642	4.6%
l	1081	3.1%
Other values (65)	10748	30.3%

Personal_Identification_Present
Categorical

High correlation

Distinct	3
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	58.3 KiB

Yes	734
No	196
	69

Length

Max length	3
Median length	3
Mean length	2.5965966
Min length	0

Characters and Unicode

Total characters	2594
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	No
2nd row	Yes
3rd row	Yes
4th row	Yes
5th row	Yes

Common Values

Value	Count	Frequency (%)
Yes	734	73.5%
No	196	19.6%
	69	6.9%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
yes	734	78.9%
no	196	21.1%

Most occurring characters

Value	Count	Frequency (%)
Y	734	28.3%
e	734	28.3%
s	734	28.3%
N	196	7.6%
o	196	7.6%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2594	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
Y	734	28.3%
e	734	28.3%
s	734	28.3%
N	196	7.6%
o	196	7.6%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2594	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
Y	734	28.3%
e	734	28.3%
s	734	28.3%
N	196	7.6%
o	196	7.6%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2594	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
Y	734	28.3%
e	734	28.3%
s	734	28.3%
N	196	7.6%
o	196	7.6%

Personal_Identification_Evidence
Text

Distinct	736
Distinct (%)	73.7%
Missing	0
Missing (%)	0.0%
Memory size	151.2 KiB

Length

Max length	252
Median length	185
Mean length	94.826827
Min length	0

Characters and Unicode

Total characters	94732
Distinct characters	83
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	735 ?
Unique (%)	73.6%

Sample

1st row
2nd row	Directly targets 'Seniors' and 'Drivers Aged 70 and Above', creating immediate relevance for this demographic.
3rd row	Readers who are LGBTQ+, advocates for civil rights, or those with military connections may strongly identify with the cadets' situation and the implications of the decision.
4th row	The terms 'drivers' and 'millions of drivers' directly target and appeal to a very large and identifiable group of readers.
5th row	Appeals directly to regular McDonald's breakfast consumers who might be affected by the change.

Value	Count	Frequency (%)
the	1011	7.0%
to	584	4.0%
of	503	3.5%
and	496	3.4%
with	377	2.6%
or	370	2.6%
a	339	2.3%
directly	288	2.0%
identify	228	1.6%
in	227	1.6%
Other values (2152)	10016	69.4%

Most occurring characters

Value	Count	Frequency (%)
	13704	14.5%
e	10282	10.9%
i	6536	6.9%
t	6368	6.7%
a	6089	6.4%
n	5916	6.2%
o	5558	5.9%
r	5127	5.4%
s	5051	5.3%
l	3739	3.9%
Other values (73)	26362	27.8%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	94732	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	13704	14.5%
e	10282	10.9%
i	6536	6.9%
t	6368	6.7%
a	6089	6.4%
n	5916	6.2%
o	5558	5.9%
r	5127	5.4%
s	5051	5.3%
l	3739	3.9%
Other values (73)	26362	27.8%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	94732	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	13704	14.5%
e	10282	10.9%
i	6536	6.9%
t	6368	6.7%
a	6089	6.4%
n	5916	6.2%
o	5558	5.9%
r	5127	5.4%
s	5051	5.3%
l	3739	3.9%
Other values (73)	26362	27.8%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	94732	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	13704	14.5%
e	10282	10.9%
i	6536	6.9%
t	6368	6.7%
a	6089	6.4%
n	5916	6.2%
o	5558	5.9%
r	5127	5.4%
s	5051	5.3%
l	3739	3.9%
Other values (73)	26362	27.8%

Interactions

Correlations

	Authority_Present	Clarity_and_Conciseness_Value	Contains_Colon	Contains_Exclamation_Mark	Contains_Hyphen	Contains_Numbers	Contains_Question_Mark	Contains_Quotes	Curiosity_Present	Economic_Benefit_Present	Ends_With_Question_Mark	Exclusivity_Present	Exclusivity_Words	Fear_Concern_Present	Hope_Optimism_Present	Indignation_Controversy_Present	Length_General_Assessment	Main_Category	Main_Classification	National_Relevance_Present	Originality_and_Differentiation_Value	Personal_Identification_Present	Prohibition_Restriction_Present	Recognized_Brand_Present	Relevance_and_Timeliness_Value	Solution_Present	Starts_With_Number	Strategic_Keyword_Usage_Value	Surprise_Awe_Present	Temporal_Urgency_Present	Visibility
Authority_Present	1.000	0.706	0.708	0.707	0.706	0.707	0.707	0.706	0.709	0.706	0.706	0.707	0.700	0.712	0.707	0.708	0.707	0.726	0.741	0.707	0.707	0.708	0.711	0.707	0.708	0.706	0.708	0.708	0.706	0.706	1.000
Clarity_and_Conciseness_Value	0.706	1.000	0.706	0.706	0.706	0.706	0.706	0.707	0.706	0.708	0.706	0.706	0.571	0.707	0.706	0.706	0.801	0.581	0.574	0.709	0.593	0.708	0.708	0.707	0.707	0.706	0.706	0.712	0.707	0.710	1.000
Contains_Colon	0.708	0.706	1.000	0.707	0.711	0.706	0.707	0.725	0.714	0.707	0.707	0.710	0.710	0.707	0.706	0.706	0.707	0.714	0.766	0.707	0.714	0.707	0.707	0.706	0.707	0.706	0.708	0.707	0.709	0.708	1.000
Contains_Exclamation_Mark	0.707	0.706	0.707	1.000	0.706	0.708	0.706	0.707	0.707	0.707	0.706	0.706	0.688	0.706	0.707	0.707	0.706	0.704	0.690	0.706	0.706	0.707	0.706	0.706	0.706	0.707	0.707	0.706	0.706	0.707	1.000
Contains_Hyphen	0.706	0.706	0.711	0.706	1.000	0.713	0.710	0.707	0.707	0.706	0.709	0.708	0.702	0.707	0.711	0.708	0.708	0.720	0.707	0.707	0.712	0.708	0.707	0.707	0.707	0.708	0.708	0.707	0.706	0.708	1.000
Contains_Numbers	0.707	0.706	0.706	0.708	0.713	1.000	0.710	0.706	0.710	0.720	0.708	0.708	0.700	0.708	0.707	0.707	0.706	0.711	0.729	0.706	0.707	0.707	0.706	0.706	0.707	0.707	0.739	0.706	0.712	0.709	1.000
Contains_Question_Mark	0.707	0.706	0.707	0.706	0.710	0.710	1.000	0.708	0.710	0.707	0.905	0.707	0.689	0.706	0.707	0.707	0.707	0.705	0.973	0.706	0.707	0.708	0.707	0.710	0.706	0.707	0.708	0.707	0.709	0.707	1.000
Contains_Quotes	0.706	0.707	0.725	0.707	0.707	0.706	0.708	1.000	0.711	0.706	0.707	0.708	0.709	0.707	0.710	0.711	0.708	0.728	0.802	0.712	0.725	0.707	0.707	0.709	0.712	0.713	0.708	0.708	0.718	0.707	1.000
Curiosity_Present	0.709	0.706	0.714	0.707	0.707	0.710	0.710	0.711	1.000	0.708	0.709	0.708	0.695	0.721	0.708	0.706	0.706	0.743	0.734	0.723	0.732	0.706	0.707	0.706	0.707	0.708	0.709	0.708	0.731	0.708	1.000
Economic_Benefit_Present	0.706	0.708	0.707	0.707	0.706	0.720	0.707	0.706	0.708	1.000	0.707	0.707	0.706	0.709	0.720	0.709	0.711	0.743	0.698	0.706	0.708	0.707	0.707	0.712	0.706	0.706	0.708	0.707	0.710	0.707	1.000
Ends_With_Question_Mark	0.706	0.706	0.707	0.706	0.709	0.708	0.905	0.707	0.709	0.707	1.000	0.707	0.689	0.706	0.706	0.707	0.706	0.707	0.885	0.707	0.707	0.707	0.707	0.708	0.706	0.706	0.707	0.707	0.708	0.706	1.000
Exclusivity_Present	0.707	0.706	0.710	0.706	0.708	0.708	0.707	0.708	0.708	0.707	0.707	1.000	0.987	0.709	0.707	0.706	0.708	0.708	0.706	0.706	0.711	0.710	0.706	0.706	0.707	0.707	0.708	0.707	0.715	0.706	1.000
Exclusivity_Words	0.700	0.571	0.710	0.688	0.702	0.700	0.689	0.709	0.695	0.706	0.689	0.987	1.000	0.696	0.699	0.698	0.717	0.283	0.250	0.698	0.580	0.709	0.702	0.698	0.708	0.695	0.690	0.689	0.706	0.701	1.000
Fear_Concern_Present	0.712	0.707	0.707	0.706	0.707	0.708	0.706	0.707	0.721	0.709	0.706	0.709	0.696	1.000	0.740	0.739	0.708	0.792	0.716	0.715	0.708	0.710	0.726	0.706	0.710	0.712	0.710	0.706	0.709	0.708	1.000
Hope_Optimism_Present	0.707	0.706	0.706	0.707	0.711	0.707	0.707	0.710	0.708	0.720	0.706	0.707	0.699	0.740	1.000	0.729	0.709	0.765	0.713	0.715	0.708	0.719	0.708	0.709	0.711	0.797	0.714	0.707	0.713	0.707	1.000
Indignation_Controversy_Present	0.708	0.706	0.706	0.707	0.708	0.707	0.707	0.711	0.706	0.709	0.707	0.706	0.698	0.739	0.729	1.000	0.711	0.748	0.719	0.712	0.718	0.709	0.711	0.708	0.706	0.717	0.708	0.707	0.717	0.706	1.000
Length_General_Assessment	0.707	0.801	0.707	0.706	0.708	0.706	0.707	0.708	0.706	0.711	0.706	0.708	0.717	0.708	0.709	0.711	1.000	0.712	0.717	0.709	0.709	0.707	0.707	0.708	0.707	0.707	0.709	0.708	0.708	0.708	1.000
Main_Category	0.726	0.581	0.714	0.704	0.720	0.711	0.705	0.728	0.743	0.743	0.707	0.708	0.283	0.792	0.765	0.748	0.712	1.000	0.307	0.787	0.602	0.756	0.743	0.753	0.708	0.778	0.723	0.708	0.748	0.712	1.000
Main_Classification	0.741	0.574	0.766	0.690	0.707	0.729	0.973	0.802	0.734	0.698	0.885	0.706	0.250	0.716	0.713	0.719	0.717	0.307	1.000	0.712	0.600	0.705	0.702	0.711	0.702	0.717	0.844	0.697	0.733	0.714	1.000
National_Relevance_Present	0.707	0.709	0.707	0.706	0.707	0.706	0.706	0.712	0.723	0.706	0.707	0.706	0.698	0.715	0.715	0.712	0.709	0.787	0.712	1.000	0.710	0.707	0.707	0.711	0.707	0.719	0.706	0.707	0.713	0.708	1.000
Originality_and_Differentiation_Value	0.707	0.593	0.714	0.706	0.712	0.707	0.707	0.725	0.732	0.708	0.707	0.711	0.580	0.708	0.708	0.718	0.709	0.602	0.600	0.710	1.000	0.724	0.706	0.709	0.706	0.709	0.707	0.711	0.808	0.706	1.000
Personal_Identification_Present	0.708	0.708	0.707	0.707	0.708	0.707	0.708	0.707	0.706	0.707	0.707	0.710	0.709	0.710	0.719	0.709	0.707	0.756	0.705	0.707	0.724	1.000	0.710	0.707	0.708	0.714	0.708	0.707	0.735	0.707	1.000
Prohibition_Restriction_Present	0.711	0.708	0.707	0.706	0.707	0.706	0.707	0.707	0.707	0.707	0.707	0.706	0.702	0.726	0.708	0.711	0.707	0.743	0.702	0.707	0.706	0.710	1.000	0.706	0.707	0.706	0.707	0.706	0.707	0.710	1.000
Recognized_Brand_Present	0.707	0.707	0.706	0.706	0.707	0.706	0.710	0.709	0.706	0.712	0.708	0.706	0.698	0.706	0.709	0.708	0.708	0.753	0.711	0.711	0.709	0.707	0.706	1.000	0.706	0.717	0.715	0.706	0.709	0.707	1.000
Relevance_and_Timeliness_Value	0.708	0.707	0.707	0.706	0.707	0.707	0.706	0.712	0.707	0.706	0.706	0.707	0.708	0.710	0.711	0.706	0.707	0.708	0.702	0.707	0.706	0.708	0.707	0.706	1.000	0.708	0.707	0.735	0.707	0.707	1.000
Solution_Present	0.706	0.706	0.706	0.707	0.708	0.707	0.707	0.713	0.708	0.706	0.706	0.707	0.695	0.712	0.797	0.717	0.707	0.778	0.717	0.719	0.709	0.714	0.706	0.717	0.708	1.000	0.708	0.708	0.715	0.707	1.000
Starts_With_Number	0.708	0.706	0.708	0.707	0.708	0.739	0.708	0.708	0.709	0.708	0.707	0.708	0.690	0.710	0.714	0.708	0.709	0.723	0.844	0.706	0.707	0.708	0.707	0.715	0.707	0.708	1.000	0.707	0.709	0.708	1.000
Strategic_Keyword_Usage_Value	0.708	0.712	0.707	0.706	0.707	0.706	0.707	0.708	0.708	0.707	0.707	0.707	0.689	0.706	0.707	0.707	0.708	0.708	0.697	0.707	0.711	0.707	0.706	0.706	0.735	0.708	0.707	1.000	0.707	0.706	1.000
Surprise_Awe_Present	0.706	0.707	0.709	0.706	0.706	0.712	0.709	0.718	0.731	0.710	0.708	0.715	0.706	0.709	0.713	0.717	0.708	0.748	0.733	0.713	0.808	0.735	0.707	0.709	0.707	0.715	0.709	0.707	1.000	0.708	1.000
Temporal_Urgency_Present	0.706	0.710	0.708	0.707	0.708	0.709	0.707	0.707	0.708	0.707	0.706	0.706	0.701	0.708	0.707	0.706	0.708	0.712	0.714	0.708	0.706	0.707	0.710	0.707	0.707	0.707	0.708	0.706	0.708	1.000	1.000
Visibility	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000

Missing values

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

	Title	Visibility	Cleaned_Headline	Main_Category	Subcategory_1	Subcategory_2	Clarity_and_Conciseness_Value	Clarity_and_Conciseness_Comment	Relevance_and_Timeliness_Value	Relevance_and_Timeliness_Comment	Strategic_Keyword_Usage_Value	Strategic_Keyword_Usage_Comment	Originality_and_Differentiation_Value	Originality_and_Differentiation_Comment	Contains_Numbers	Contains_Quotes	Contains_Question_Mark	Contains_Colon	Contains_Hyphen	Contains_Exclamation_Mark	Starts_With_Number	Ends_With_Question_Mark	Length_General_Assessment	Length_Number_of_Characters	Emphatic_Capitalization_Usage	Main_Classification	Comment_Type_Justification	Temporal_Urgency_Present	Temporal_Urgency_Words	Exclusivity_Present	Exclusivity_Words	Authority_Present	Authority_Words	Solution_Present	Solution_Words	Economic_Benefit_Present	Economic_Benefit_Words	Prohibition_Restriction_Present	Prohibition_Restriction_Words	National_Relevance_Present	National_Relevance_Words	Recognized_Brand_Present	Recognized_Brand_Words	Curiosity_Present	Curiosity_Evidence	Fear_Concern_Present	Fear_Concern_Evidence	Surprise_Awe_Present	Surprise_Awe_Evidence	Indignation_Controversy_Present	Indignation_Controversy_Evidence	Hope_Optimism_Present	Hope_Optimism_Evidence	Personal_Identification_Present	Personal_Identification_Evidence
0	Ford is forced to immediately shut down factories and halt car production as CEO admits 'day to day' struggle for brand	22572066	Ford is forced to immediately shut down factories and halt car production as CEO admits 'day to day' struggle for brand	Finance_and_Business	Companies & Entrepreneurship	N/A	High	The main message is very clear and easy to understand, detailing Ford's production halt and the CEO's admission of struggle.	High	The headline discusses a significant event for a major global brand, which is highly relevant and timely for business and general news audiences.	High	Key terms like 'Ford', 'factories', 'car production', 'CEO', and 'struggle' are used effectively, making the headline highly discoverable and appealing.	Medium	While the subject matter is impactful, the phrasing is a standard news report style, not particularly unique in its linguistic approach.	No	Yes	No	No	No	No	No	No	Adequate	105	No	Declarative Simple	The headline presents a series of factual statements about Ford's situation and the CEO's admission without posing a question, using urgency markers, or comparing elements.	Yes	["immediately"]	No	[]	Yes	["CEO admits"]	No	[]	No	[]	No	[]	No	[]	Yes	["Ford"]	No		Yes	The phrases 'forced to immediately shut down factories', 'halt car production', and 'struggle for brand' evoke concern about economic stability and the future of a major company.	No		No		No		No
1	New U.S. Driving License Rule for Seniors Begins July 2025 - Essential Changes for Drivers Aged 70 and Above	21331409	New U.S. Driving License Rule for Seniors Begins July 2025 - Essential Changes for Drivers Aged 70 and Above	News_and_Current_Events	Politics	Government	High	The main message is exceptionally clear, detailing who, what, and when without ambiguity.	High	Highly relevant to a specific, large demographic (seniors/drivers) and provides timely information about a future change.	High	Contains highly relevant keywords like 'U.S. Driving License Rule', 'Seniors', 'July 2025', 'Essential Changes', and 'Drivers Aged 70 and Above'.	Medium	While a standard news announcement format, the specific details make it distinct, yet it lacks a unique angle or creative phrasing.	Yes	No	No	No	Yes	No	No	No	Adequate	101	No	Declarative Simple	The headline directly states a new rule and its implications, serving as a straightforward announcement.	Yes	["Begins July 2025"]	No	[]	No	[]	No	[]	No	[]	No	[]	Yes	["U.S."]	No	[]	Yes	The phrase 'Essential Changes' prompts readers to seek information on what those changes entail.	No		No		No		No		Yes	Directly targets 'Seniors' and 'Drivers Aged 70 and Above', creating immediate relevance for this demographic.
2	Cadets who met all Air Force Academy graduation standards denied commissions because they’re transgender	19344936	Cadets who met all Air Force Academy graduation standards denied commissions because they’re transgender	News_and_Current_Events	Politics	Government	High	The main message is very clear and direct, stating precisely what occurred and why.	High	The topic of transgender rights and military policy is highly current and relevant, resonating with ongoing social and political discussions.	High	Uses strong, specific keywords like 'Cadets', 'Air Force Academy', 'denied commissions', and 'transgender', which are highly relevant to the subject matter and discoverable.	Medium	The specific event described is notable, though the broader subject of transgender individuals in the military has been discussed before. It offers a distinct event within a known debate.	No	No	No	No	No	No	No	No	Adequate	106	No	Declarative Simple	The headline makes a direct statement about an event, presenting it as a fact without using a question, direct quote, or emphasizing urgency.	No	[]	No	[]	No	[]	No	[]	No	[]	Yes	["denied"]	No	[]	No	[]	No		No		No		Yes	The phrase 'denied commissions because they’re transgender' directly implies a potential act of discrimination, which is highly likely to provoke indignation and controversy among various audiences.	No		Yes	Readers who are LGBTQ+, advocates for civil rights, or those with military connections may strongly identify with the cadets' situation and the implications of the decision.
3	The DMV confirms it - The United States is ending the benefit that allowed drivers to drive with an expired license without immediate renewal - millions of drivers will have to renew urgently	18797641	The DMV confirms it - The United States is ending the benefit that allowed drivers to drive with an expired license without immediate renewal - millions of drivers will have to renew urgently	News_and_Current_Events	Politics	Government	High	The main message is very clear and direct, stating the change, its source, and its impact.	High	Highly relevant as it affects a large demographic ('millions of drivers') and requires urgent action, indicating timeliness.	High	Uses strong keywords like 'DMV', 'United States', 'expired license', 'drivers', and 'renew urgently', which are highly searchable and relevant.	Medium	While the specific policy change is unique, the headline structure (authority confirms X - consequence) is common. Its strength lies in the immediate, widespread impact.	Yes	No	No	No	Yes	No	No	No	Adequate	173	No	Urgency	The headline clearly states a new policy confirmed by an authority and strongly emphasizes the immediate, widespread need for action, particularly with the phrase 'renew urgently' affecting 'millions of drivers'.	Yes	["immediate","urgently"]	No	[]	Yes	["DMV","confirms"]	No	[]	No	[]	Yes	["ending the benefit","without immediate renewal"]	Yes	["United States"]	Yes	["DMV"]	No		Yes	The phrase 'millions of drivers will have to renew urgently' directly implies potential negative consequences or significant inconvenience if the reader fails to act, evoking concern.	No		No		No		Yes	The terms 'drivers' and 'millions of drivers' directly target and appeal to a very large and identifiable group of readers.
4	McDonald's Removing 1 Breakfast Menu Item for Good on July 2	16353543	McDonald's Removing 1 Breakfast Menu Item for Good on July 2	Gastronomy	Restaurants & Chefs	N/A	High	The message is clear and to the point, identifying the brand, action, and date.	High	Highly relevant for McDonald's customers and tied to a specific future date, July 2.	High	Uses strong keywords like 'McDonald's', 'Breakfast Menu', and 'Removing', appealing to target audience interests.	Medium	While common for fast-food news, the specificity of '1 Breakfast Menu Item' and 'for Good' provides some differentiation.	Yes	No	No	No	No	No	No	No	Adequate	57	No	Declarative Simple	The headline directly states a fact without posing a question, using a quote, or indicating urgency beyond the date.	No	[]	No	[]	No	[]	No	[]	No	[]	No	[]	No	[]	Yes	["McDonald's"]	Yes	The headline creates an information gap by not revealing which specific breakfast item is being removed.	Yes	The phrase 'Removing... for Good' could evoke concern among customers about a favorite item.	No		No		No		Yes	Appeals directly to regular McDonald's breakfast consumers who might be affected by the change.
5	The DMV confirms it—people over 70 will have to meet new requirements to keep their driver's license in the United States	15318643	The DMV confirms it—people over 70 will have to meet new requirements to keep their driver's license in the United States	News_and_Current_Events	Politics	Government	High	The main message about new DMV requirements for older drivers is very clear and easy to understand.	High	Driving regulations, especially those affecting a specific age group, are highly relevant to a large demographic and are a recurring topic of public interest.	High	Keywords like 'DMV', '70', 'driver's license', and 'United States' are highly relevant and likely to be searched for or noticed by the target audience.	Medium	The headline is specific about the authority ('DMV confirms it') and the demographic, which gives it some differentiation from generic news, but the structure is standard.	Yes	No	No	No	Yes	No	No	No	Adequate	106	No	Declarative Simple	The headline directly states a fact confirmed by an authority, informing the reader of a new regulation without posing a question or implying a mystery.	No	[]	No	[]	Yes	["confirms","DMV"]	No	[]	No	[]	Yes	["will have to meet new requirements","keep their driver's license"]	Yes	["United States"]	Yes	["DMV"]	Yes	The mention of 'new requirements' without specifying them creates an information gap, prompting readers to click to learn what these changes entail.	Yes	The phrase 'will have to meet new requirements to keep their driver's license' can evoke concern or anxiety for older drivers about their ability to retain their driving privileges and independence.	No		No		No		Yes	The headline directly addresses and impacts 'people over 70', creating strong personal identification for individuals in that age group and their families.
6	Goodbye to retirement at 65: Social Security sets a new retirement age from 2026	14627061	Goodbye to retirement at 65: Social Security sets a new retirement age from 2026	Finance_and_Business	Personal_Finance	Retirement	High	The main message is immediately clear: the retirement age is changing.	High	The topic of retirement age and Social Security is highly relevant to a broad audience and includes a specific future date ('from 2026') indicating timeliness.	High	Key terms like 'retirement,' 'Social Security,' and 'retirement age' are highly strategic and likely to be searched by interested individuals.	Medium	While retirement changes are common news, the opening phrase 'Goodbye to retirement at 65' adds an impactful and somewhat unique framing.	Yes	No	No	Yes	No	No	No	No	Adequate	70	No	Declarative Simple	The headline presents a direct statement of fact about a change in the retirement age.	Yes	["from 2026"]	No	[]	Yes	["Social Security"]	No	[]	No	[]	No	[]	No	[]	Yes	["Social Security"]	Yes	The phrase 'Goodbye to retirement at 65' immediately piques curiosity about what the new age will be and why the change is happening.	Yes	The headline could evoke concern among readers about their financial future and the implications of a later retirement.	Yes	The news of a fundamental shift in retirement age, a long-established concept, can be surprising.	Yes	Changes to social programs like retirement age frequently generate public debate and potential indignation.	No		Yes	The topic of retirement and Social Security directly affects the financial planning and future of many individuals, leading to strong personal identification.
7	Elon Musk gave Apple 72 hours to accept his $5 billion offer. Tim Cook said no, so Elon followed through with his threat.	13507301	Elon Musk gave Apple 72 hours to accept his $5 billion offer. Tim Cook said no, so Elon followed through with his threat.	Finance_and_Business	Companies & Entrepreneurship	N/A	High	The main message is very clear, detailing the involved parties, the offer, the rejection, and the subsequent action, without ambiguity.	High	Features highly relevant and frequently discussed figures (Elon Musk, Tim Cook) and companies (Apple), ensuring strong general interest.	High	Includes prominent keywords such as 'Elon Musk', 'Apple', 'Tim Cook', and '$5 billion', which are highly searchable and appealing to a broad audience.	High	The narrative of a public challenge, rejection, and explicit follow-through on a 'threat' provides a unique and dramatic angle that stands out.	Yes	No	No	No	No	No	No	No	Adequate	104	No	Declarative Simple	The headline presents a straightforward statement of facts without posing a question, quoting directly, or explicitly indicating urgency.	Yes	["72 hours"]	No	[]	No	[]	No	[]	Yes	["$5 billion"]	No	[]	No	[]	Yes	["Elon Musk","Apple","Tim Cook"]	Yes	The phrase 'followed through with his threat' creates an information gap, compelling the reader to wonder what the threat was and its consequences.	No		Yes	The audacious $5 billion offer with a short ultimatum and the dramatic follow-through on a 'threat' can evoke surprise.	Yes	The nature of the 'threat' and its execution can spark debate or strong opinions about the involved parties' actions.	No		No
8	90s Country Icon’s Teeth Fall Out Mid-Performance During Washington Concert: “The Show Must Go On”	13339952	90s Country Icon’s Teeth Fall Out Mid-Performance During Washington Concert: “The Show Must Go On”	Entertainment_and_Culture	Celebrities_and_Influencers	N/A	High	The main message is straightforward and easy to understand, describing a highly unusual and specific event.	High	The headline describes an unusual and attention-grabbing incident involving a celebrity, which is highly relevant to current interests in entertainment news.	High	Keywords like "90s Country Icon," "Teeth Fall Out," and "Mid-Performance" are highly specific and engaging, likely to capture audience attention.	High	The event described is extremely unusual and therefore the headline is highly original and stands out from typical news.	Yes	Yes	No	Yes	No	No	Yes	No	Adequate	101	Yes, "The Show Must Go On", justified use as it is a direct quote.	Direct Quote	The headline prominently features a direct quote from the event, which is essential to its content and impact.	No	[]	No	[]	No	[]	No	[]	No	[]	No	[]	Yes	["Washington"]	No	[]	Yes	The phrase "Teeth Fall Out Mid-Performance" creates a strong information gap and immediately makes the reader curious about the circumstances.	No		Yes	The highly unexpected and bizarre nature of "Teeth Fall Out Mid-Performance" elicits surprise and possibly a sense of awe at the unusual incident.	No		No		No
9	Americans who own refrigerators from 3 brands to get $300 from settlement	13255807	Americans who own refrigerators from 3 brands to get $300 from settlement	Finance_and_Business	Personal_Finance	N/A	High	The main message is very clear: certain Americans will receive money due to a settlement.	High	Directly impacts a large segment of the population (Americans who own specific appliances) and offers a tangible benefit (money from a settlement).	High	Includes highly relevant keywords like 'Americans', 'refrigerators', 'brands', '$300', and 'settlement', which are likely search terms for interested individuals.	Medium	While the specific settlement details are unique, the headline structure of 'who gets money from X' is a common news format. The unnamed brands reduce its immediate differentiation.	Yes	No	No	No	No	No	No	No	Adequate	70	No	Declarative Simple	The headline presents a direct and straightforward statement of fact without asking a question, making a comparison, or implying urgency.	No	[]	No	[]	No	[]	No	[]	Yes	["$300","settlement"]	No	[]	Yes	["Americans"]	No	[]	Yes	The mention of '3 brands' creates a curiosity gap, prompting readers to click to find out which brands are involved.	No		No		No		Yes	The prospect of receiving $300 from a settlement evokes a sense of hope and financial optimism for those who qualify.	Yes	Americans who own refrigerators' directly targets a broad demographic, encouraging self-identification and relevance.

	Title	Visibility	Cleaned_Headline	Main_Category	Subcategory_1	Subcategory_2	Clarity_and_Conciseness_Value	Clarity_and_Conciseness_Comment	Relevance_and_Timeliness_Value	Relevance_and_Timeliness_Comment	Strategic_Keyword_Usage_Value	Strategic_Keyword_Usage_Comment	Originality_and_Differentiation_Value	Originality_and_Differentiation_Comment	Contains_Numbers	Contains_Quotes	Contains_Question_Mark	Contains_Colon	Contains_Hyphen	Contains_Exclamation_Mark	Starts_With_Number	Ends_With_Question_Mark	Length_General_Assessment	Length_Number_of_Characters	Emphatic_Capitalization_Usage	Main_Classification	Comment_Type_Justification	Temporal_Urgency_Present	Temporal_Urgency_Words	Exclusivity_Present	Exclusivity_Words	Authority_Present	Authority_Words	Solution_Present	Solution_Words	Economic_Benefit_Present	Economic_Benefit_Words	Prohibition_Restriction_Present	Prohibition_Restriction_Words	National_Relevance_Present	National_Relevance_Words	Recognized_Brand_Present	Recognized_Brand_Words	Curiosity_Present	Curiosity_Evidence	Fear_Concern_Present	Fear_Concern_Evidence	Surprise_Awe_Present	Surprise_Awe_Evidence	Indignation_Controversy_Present	Indignation_Controversy_Evidence	Hope_Optimism_Present	Hope_Optimism_Evidence	Personal_Identification_Present	Personal_Identification_Evidence
989	New tax hikes on a variety of items coming to Illinois next month. Here's what prices are going up and when	590578	New tax hikes on a variety of items coming to Illinois next month. Here's what prices are going up and when	Finance_and_Business	Personal Finance	N/A	High	The headline clearly states the subject (tax hikes), location (Illinois), and implication (prices going up), and when (next month).	High	The topic of tax hikes and rising prices is highly relevant to personal finance and timely due to the 'next month' mention.	High	Uses strong keywords like 'tax hikes', 'Illinois', and 'prices going up' which are highly relevant and searchable.	Medium	While tax hike news is common, the second sentence directly addressing 'what prices' and 'when' adds a valuable and somewhat unique angle, though the core topic is not novel.	No	No	No	Yes	No	No	No	No	Adequate	101	No	Mystery/Revelation	The headline, particularly the second part 'Here's what prices are going up and when', promises to reveal specific information, creating an information gap that readers will want to fill.	Yes	["next month"]	No	[]	No	[]	No	[]	No	[]	No	[]	Yes	["Illinois"]	No	[]	Yes	Here's what prices are going up and when	Yes	New tax hikes, prices are going up	No		Yes	New tax hikes	No		Yes	Illinois, prices are going up
990	Illinois residents' information accessed in data breach, Healthcare and Family Services says	589230	Illinois residents' information accessed in data breach, Healthcare and Family Services says	News_and_Current_Events	Crime & Judicial	N/A	High	The main message is clear and easy to understand, directly stating the event and its source.	High	Data breaches are a highly relevant and timely topic, especially for the affected demographic.	High	The headline effectively uses relevant keywords such as "Illinois residents", "information accessed", and "data breach".	Medium	While data breaches are common, the specific mention of "Illinois residents" and the government agency provides some differentiation.	No	No	No	No	No	No	No	No	Adequate	84	No	Attribution ('according to', 'reveals')	The headline explicitly states the source of the information, "Healthcare and Family Services says", making it an attribution type.	No	[]	No	[]	Yes	["says"]	No	[]	No	[]	No	[]	Yes	["Illinois"]	Yes	["Healthcare and Family Services"]	No		Yes	The phrase "data breach" and "information accessed" immediately triggers concern regarding personal security and privacy.	No		No		No		Yes	The mention of "Illinois residents" directly targets and makes the headline relevant to a specific audience.
991	A Buc-ee’s competitor is marking its territory across Texas	588887	A Buc-ee’s competitor is marking its territory across Texas	Finance_and_Business	Companies & Entrepreneurship	Competition	High	The headline is very clear and easy to understand.	High	Highly relevant to Texans and those interested in travel/business news due to the popularity of Buc-ee's.	High	Uses strong keywords like "Buc-ee's", "competitor", and "Texas" which are highly appealing to the target audience.	High	The phrase "marking its territory" adds a unique and evocative angle, making it stand out.	No	No	No	No	Yes	No	No	No	Adequate	55	No	Declarative Simple	The headline makes a straightforward statement of fact without asking a question, quoting someone, or indicating urgency.	No	[]	No	[]	No	[]	No	[]	No	[]	No	[]	Yes	["Texas"]	Yes	["Buc-ee’s"]	Yes	The mention of a "Buc-ee’s competitor" and "marking its territory" creates an information gap, making the reader curious about who the competitor is and what they are doing.	No		No		No		No		Yes	The headline directly appeals to individuals who are familiar with or reside in Texas and are aware of Buc-ee's.
992	Sam's Club sends a last-minute message to its members: It's here already	587557	Sam's Club sends a last-minute message to its members: It's here already	Finance_and_Business	Companies & Entrepreneurship	N/A	High	The main message is clear about the sender and recipient, and implies an important arrival.	High	The 'last-minute' and 'already' phrases create immediate relevance and urgency for Sam's Club members.	Medium	Uses "Sam's Club" and "members" which are relevant, but the ambiguous "It's here already" might not optimize for specific searches.	Medium	The specific phrasing with the intriguing "It's here already" provides a unique angle compared to a purely informative headline.	No	Yes	No	Yes	Yes	No	No	No	Adequate	70	No	Mystery/Revelation	The headline hints at the arrival of something significant ("It's here already") but doesn't reveal what it is, thereby building anticipation and curiosity.	Yes	["last-minute","already"]	No	[]	No	[]	No	[]	No	[]	No	[]	No	[]	Yes	["Sam's Club"]	Yes	The phrase 'It's here already' creates an information gap, making the reader wonder what 'it' is.	No		No		No		No		Yes	The phrase 'to its members' directly targets a specific audience, making the message feel personal and relevant to them.
993	Jennifer Aniston's home features the most glamorous garden lounge we've ever seen – it is five-star perfection	586529	Jennifer Aniston's home features the most glamorous garden lounge we've ever seen – it is five-star perfection	Home_and_Lifestyle	Decoration_and_Interior_Design	N/A	High	The message is very clear and easy to understand, directly stating the subject and its perceived quality.	High	Celebrity homes and interior design are consistently high-interest evergreen topics.	High	Uses strong keywords like 'Jennifer Aniston', 'home', 'garden lounge', and descriptive terms like 'glamorous' and 'five-star perfection' appealing to the target audience.	Medium	While celebrity home features are common, the strong superlative claim of "most glamorous...we've ever seen" provides a level of differentiation.	Yes	No	No	No	No	No	No	No	Adequate	80	No	Superlative	The headline explicitly uses superlative language such as "most glamorous" and "five-star perfection" to describe the subject.	No	[]	No	[]	No	[]	No	[]	No	[]	No	[]	No	[]	Yes	["Jennifer Aniston"]	Yes	The phrase "the most glamorous garden lounge we've ever seen – it is five-star perfection" creates an information gap, making readers curious to see what makes it so exceptional.	No		Yes	The strong superlative "most glamorous" and "five-star perfection" is intended to evoke a sense of awe and wonder at the described feature.	No		No		Yes	Readers can easily imagine and aspire to have such a glamorous feature in their own homes, leading to personal identification with the desirable element.
994	18 High-Protein No-Cook Recipes for Dinner Tonight	585428	18 High-Protein No-Cook Recipes for Dinner Tonight	Gastronomy	Recipes	Main Courses	High	The main message is very clear: 18 high-protein, no-cook recipes for dinner tonight. No ambiguity.	High	Highly relevant to current interests in healthy eating and convenience, with a clear timely hook ("Tonight").	High	Uses strong keywords like "High-Protein", "No-Cook", "Recipes", and "Dinner Tonight" that align with user searches and interests.	Medium	While recipe headlines are common, the specific combination of "High-Protein" and "No-Cook" offers a somewhat unique and appealing angle.	Yes	No	No	No	Yes	No	Yes	No	Adequate	53	No	List/Numbered	The headline explicitly starts with a number (18), indicating a list of items.	Yes	["Tonight"]	No	[]	No	[]	Yes	["Recipes","No-Cook"]	No	[]	Yes	["No-Cook"]	No	[]	No	[]	Yes	The specific number "18" and the unique combination of "High-Protein" and "No-Cook" create an information gap that sparks curiosity about the recipes themselves.	No		No		No		Yes	Offers a convenient, healthy, and easy solution for dinner, evoking a sense of relief and positivity.	Yes	Directly addresses a common need ("for Dinner Tonight") and targets individuals seeking healthy ("High-Protein") and easy ("No-Cook") meal solutions, making it highly relatable.
995	Map Shows the 50 Best School Districts Across US	585417	Map Shows the 50 Best School Districts Across US	Education_and_Guides	Academic Advice	N/A	High	The main message is very clear and easy to understand.	High	Education quality and school rankings are consistently relevant to a broad audience.	High	Uses highly relevant keywords like "Map", "50 Best", "School Districts", and "US".	Medium	While listicles are common, the "Map Shows" aspect adds a slight visual and informative differentiator.	Yes	No	No	No	No	No	No	No	Adequate	46	No	List/Numbered ('50 Best')	The headline explicitly mentions "the 50 Best", classifying it as a numbered list and superlative.	No	[]	No	[]	No	[]	No	[]	No	[]	No	[]	Yes	["US"]	No	[]	Yes	The headline creates an information gap by promising to reveal the "50 Best School Districts" that a map shows.	No		No		No		Yes	The phrase "Best School Districts" appeals to the hope of finding quality education and a positive future.	Yes	The topic of school districts in the "US" directly appeals to parents, students, and educators residing in the country.
996	GPS-based speed limiter will be mandatory ― One state approves it and announces the date	585267
997	McDonald's Is Bringing Back a Sandwich We Never Thought We'd See Again	583765	McDonald's Is Bringing Back a Sandwich We Never Thought We'd See Again	Gastronomy	Restaurants & Chefs	N/A	High	The main message is immediately clear and easy to understand.	High	Food news, especially about popular fast-food chains and nostalgic items, generally has high relevance and evergreen interest.	High	Uses strong, relevant keywords like 'McDonald's' and 'Sandwich', which are highly searchable and appealing to the target audience.	Medium	While the theme of a returning menu item is common, the phrase "We Never Thought We'd See Again" adds a unique angle and creates intrigue.	No	No	No	No	No	No	No	No	Adequate	66	No	Mystery/Revelation ('secret', 'truth')	The headline creates a strong sense of mystery and anticipation by implying the return of a popular item that was believed to be gone forever, compelling the reader to discover what it is.	No	[]	No	[]	No	[]	No	[]	No	[]	No	[]	No	[]	Yes	["McDonald's"]	Yes	The phrase "We Never Thought We'd See Again" creates an information gap, making readers curious about which sandwich is returning.	No		Yes	The unexpected return implied by "We Never Thought We'd See Again" evokes surprise and a sense of delight for fans of the item.	No		Yes	For fans of the particular sandwich, its return brings a sense of hope and optimism about being able to enjoy it again.	Yes	The use of "We" directly includes the reader, fostering a sense of shared nostalgia and anticipation for the returning item.
998	This City Was Just Named the No. 1 Place to Live in the U.S. for Affordability and Cost of Living	582886

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